System and method for selectively verifying algorithmically populated shopping carts

ABSTRACT

An apparatus includes a memory and a processor. The memory stores a set of inputs, an algorithmic shopping cart, and a machine learning algorithm. The set of inputs includes information collected from sensors located in a physical store during a shopping session of a person. The algorithmic shopping cart includes items determined by an algorithm, based on the set of inputs, to have been selected during the shopping session. The machine learning algorithm is configured to use the set of inputs to select between using the algorithmic shopping cart and using a virtual shopping cart to process a transaction associated with the shopping session. The processor uses the machine learning algorithm to determine, based on the set of inputs, to use the algorithmic shopping cart to process the transaction. In response, the processor generates a receipt based on the algorithmic shopping cart. The processor sends the receipt to the person.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of:

U.S. patent application Ser. No. 16/664,470 filed Oct. 25, 2019, by Matthew Raymond Magee et al., and entitled “CUSTOMER-BASED VIDEO FEED”;

U.S. patent application Ser. No. 16/664,490 filed Oct. 25, 2019, by Shahmeer Ali Mirza et al., and entitled “SYSTEM AND METHOD FOR PRESENTING A VIRTUAL STORE SHELF THAT EMULATES A PHYSICAL STORE SHELF”,

U.S. patent application Ser. No. 16/938,676 filed Jul. 24, 2020, by Shahmeer Ali Mirza et al., and entitled “FEEDBACK AND TRAINING FOR A MACHINE LEARNING ALGORITHM CONFIGURED TO DETERMINE CUSTOMER PURCHASES DURING A SHOPPING SESSION ATA PHYSICAL STORE”, which is a continuation of U.S. patent application Ser. No. 16/794,083 filed Feb. 18, 2020, by Shahmeer Ali Mirza et al., and entitled “FEEDBACK AND TRAINING FOR A MACHINE LEARNING ALGORITHM CONFIGURED TO DETERMINE CUSTOMER PURCHASES DURING A SHOPPING SESSION AT A PHYSICAL STORE”, now U.S. Pat. No. 10,810,428 issued Oct. 20, 2020, which is a continuation of U.S. patent application Ser. No. 16/663,564 filed Oct. 25, 2019, by Shahmeer Ali Mirza et al., and entitled “FEEDBACK AND TRAINING FOR A MACHINE LEARNING ALGORITHM CONFIGURED TO DETERMINE CUSTOMER PURCHASES DURING A SHOPPING SESSION AT A PHYSICAL STORE”, now U.S. Pat. No. 10,607,080 issued Mar. 31, 2020;

U.S. patent application Ser. No. 17/021,011 filed Sep. 15, 2020, by Matthew Raymond Magee et al., entitled “SYSTEM AND METHOD FOR POPULATING A VIRTUAL SHOPPING CART BASED ON VIDEO OF A CUSTOMERS SHOPPING SESSION AT A PHYSICAL STORE”, which is a continuation of U.S. patent application Ser. No. 16/663,589 filed Oct. 25, 2019, by Matthew Raymond Magee et al., entitled “SYSTEM AND METHOD FOR POPULATING A VIRTUAL SHOPPING CART BASED ON VIDEO OF A CUSTOMER′S SHOPPING SESSION AT A PHYSICAL STORE”, and

U.S. patent application Ser. No. 16/664,529 filed Oct. 25, 2019, by Matthew Raymond Magee et al., entitled “TOOL FOR GENERATING A VIRTUAL STORE THAT EMULATES A PHYSICAL STORE”, which are all incorporated herein by reference.

TECHNICAL FIELD

This disclosure relates generally to remote monitoring techniques, and more particularly, to a system and method for selectively verifying algorithmically populated shopping carts.

BACKGROUND

During a traditional shopping session in a physical store, a customer selects items from shelves located within the store and then presents those items to a cashier. The cashier generates a bill for the items and receives payment from the customer. Any cameras located within the store are typically present for security purposes.

SUMMARY

Shopping sessions in traditional stores may be associated with several inefficiencies for both the customers and the store owners. For example, during busy periods within a store, a customer may spend a considerable amount of time waiting in line to pay the cashier for the items he/she selected. The time spent waiting may even exceed the total amount of time that the customer spent selecting the items. This may lead to customer frustration and potentially to a loss of repeat customer business. As another example, traditional stores typically rely on the presence of one or more employees to act as cashiers within the stores. Even when the store is otherwise empty, such employees are nevertheless present, in case a customer happens to enter the store to make a purchase. As a result, outside of peak business hours, much of a cashier's time within a store may be spent idle.

This disclosure contemplates a virtual store tool that addresses one or more of the above technical problems. The tool generates a virtual store configured to emulate a physical store. The tool also generates a set of videos from camera feeds received from cameras located in the physical store, to track a customer during a shopping session in the physical store. In certain embodiments, the tool then uses the virtual store and the videos of the shopping session in the physical store to generate a virtual shopping cart, storing a set of items configured to emulate the items selected by the customer in the physical store. Accordingly, the tool may use the virtual shopping cart to charge the customer for his/her purchases. In some embodiments, the tool may also be used in conjunction with an algorithm trained to determine the items selected by a customer during a shopping session in a physical store, based on inputs received from sensors located in the physical store. In such embodiments, the tool uses the virtual store and the videos of the shopping session in the physical store to verify the determination made by the algorithm. Certain embodiments of the tool are described below.

According to one embodiment, an apparatus includes an interface, a display, a memory, and a hardware processor communicatively coupled to the memory and the display. The interface receives a first video feed. The first video feed includes a first camera feed corresponding to a first camera located in a physical store and a second camera feed corresponding to a second camera located in the physical store. The first camera is directed at a first location in the physical store. The second camera is directed at a second location in the physical store. The hardware processor stores a first video segment in the memory. The first video segment is assigned to a first person and captures a portion of a shopping session of the first person in the physical store occurring during a time interval between a starting timestamp and an ending timestamp. The first video segment includes a first camera feed segment corresponding to a recording of the first camera feed from the starting timestamp to the ending timestamp, and a second camera feed segment corresponding to a recording of the second camera feed from the starting timestamp to the ending timestamp. The processor also assigns a first slider bar to the first video segment.

Playback of the first camera feed segment and the second camera feed segment is synchronized and the first slider bar controls a playback progress of the first camera feed segment and the second camera feed segment. The processor additionally displays the first camera feed segment and a first copy of the first slider bar in a first region of the display. The processor further displays the second camera feed segment and a second copy of the first slider bar in a second region of the display. The processor also receives an instruction from at least one of the first copy of the first slider bar and the second copy of the first slider bar to adjust the playback progress of the first camera feed segment and the second camera feed segment. In response to receiving the instruction, the processor adjusts the playback progress of the first camera feed segment and the second camera feed segment.

According to another embodiment, an apparatus includes a display, an interface, and a hardware processor communicatively coupled to the display. The interface receives a rack camera feed from a rack camera located in a physical store. The rack camera is directed at a first physical rack of a set of physical racks located in the physical store. The hardware processor displays, in a first region of the display, a virtual layout of a virtual store. The virtual layout is configured to emulate a physical layout of the physical store. The virtual layout includes a first virtual rack assigned to a first physical rack and a second virtual rack assigned to a second physical rack. Here, an arrangement of the first virtual rack and the second virtual rack in the virtual layout is configured to emulate an arrangement of the first physical rack and the second physical rack in the physical layout.

The processor also receives an indication of an event associated with the first physical rack. The event includes a person located in the physical store interacting with the first physical rack. In response to receiving the indication of the event associated with the first physical rack, the processor displays, in a second region of the display, the first virtual rack. The first virtual rack includes a first virtual shelf and a second virtual shelf. The first virtual shelf includes a first virtual item and the second virtual shelf includes a second virtual item. The first virtual item includes a graphical representation of a first physical item located on a first physical shelf of the first physical rack and the second virtual item includes a graphical representation of a second physical item located on a second physical shelf of the first physical rack. The processor additionally displays, in a third region of the display, a rack video segment corresponding to a recording of the rack camera feed from a starting timestamp to an ending timestamp. The rack video segment depicts the event associated with the first physical rack.

According to another embodiment, an apparatus includes a display, an interface, and a hardware processor communicatively coupled to the display. The interface receives a rack video from a rack camera located in a physical store. The rack camera is directed at a first physical rack of a set of physical racks located in the physical store. The rack camera captures video of the first physical rack during a shopping session of a person in the physical store. The processor displays, in a first region of the display, a first virtual rack that emulates the first physical rack. The first virtual rack includes a first virtual shelf and a second virtual shelf. The first virtual shelf includes a first virtual item and the second virtual shelf includes a second virtual item. The first virtual item includes a graphical representation of a first physical item located on a first physical shelf of the first physical rack and the second virtual item includes a graphical representation of a second physical item located on a second physical shelf of the first physical rack.

The processor also displays, in a second region of the display, the rack video. The rack video depicts an event including the person interacting with the first physical rack. The processor additionally displays, in a third region of the display, a virtual shopping cart. The processor further receives information associated with the event. The information identifies the first virtual item, and the rack video depicts that the person selected the first physical item while interacting with the first physical rack. In response to receiving the information associated with the event, the processor stores the first virtual item in the virtual shopping cart.

According to another embodiment, an apparatus configured to create a virtual layout of a virtual store to emulate a physical layout of a physical store includes a memory and a hardware processor communicatively coupled to the memory. The hardware processor receives a first physical position and a first physical orientation associated with a first physical rack located in the physical store. In response to receiving the first physical position and the first physical orientation, the processor places a first virtual rack at a first virtual position and with a first virtual orientation on the virtual layout. The first virtual position of the first virtual rack on the virtual layout represents the first physical position of the first physical rack on the physical layout and the first virtual orientation of the first virtual rack on the virtual layout represents the first physical orientation of the first physical rack on the physical layout. The processor also receives a first virtual item associated with a first physical item located on a first physical shelf of the first physical rack. In response to receiving the first virtual item, the processor places the first virtual item on a first virtual shelf of the first virtual rack. The first virtual shelf of the first virtual rack represents the first physical shelf of the first physical rack.

The processor additionally receives a second virtual item associated with a second physical item located on a second physical shelf of the first physical rack. In response to receiving the second virtual item, the processor places the second virtual item on a second virtual shelf of the first virtual rack. The second virtual shelf of the first virtual rack represents the second physical shelf of the first physical rack. The processor further assigns a first rack camera located in the physical store to the first virtual rack. The first rack camera captures video that includes the first physical rack. The processor also stores the virtual layout in the memory.

According to another embodiment, an apparatus includes a hardware processor. The processor receives an algorithmic shopping cart that includes a first set of items. The first set of items is determined by an algorithm to have been selected by a first person during a shopping session in a physical store, based on a set of inputs received from sensors located within the physical store. The processor also receives a virtual shopping cart that includes a second set of items associated with the shopping session. Video of the shopping session was captured by a set of cameras located in the physical store. The video depicts the person selecting the second set of items. The processor additionally compares the algorithmic shopping cart to the virtual shopping cart. In response to comparing the algorithmic shopping cart to the virtual shopping cart, the processor determines that a discrepancy exists between the algorithmic shopping cart and the virtual shopping cart. The processor further determines a subset of the set of inputs associated with the discrepancy. The processor also attaches metadata to the subset. The metadata explains the discrepancy. The processor additionally uses the subset to train the algorithm.

According to another embodiment, an apparatus includes a memory and a hardware processor communicatively coupled to the memory. The memory stores a first set of inputs, a first algorithmic shopping cart, and instructions corresponding to a machine learning algorithm. The first set of inputs includes information collected from sensors located in a physical store during a shopping session of a first person in the physical store. The first algorithmic shopping cart includes a first set of items. The first set of items was determined by an algorithm, based on the first set of inputs, to have been selected by the first person during the shopping session of the first person. The instructions corresponding to the machine learning algorithm are configured, when implemented by the hardware processor, to use the first set of inputs to select between using the first algorithmic shopping cart to process a first transaction and using a first virtual shopping cart to process the first transaction. Here, the first transaction is associated with the shopping session of the first person and the first virtual shopping cart includes items associated with the shopping session of the first person.

The processor uses the machine learning algorithm to determine, based on the first set of inputs, to use the first algorithmic shopping cart to process the first transaction. Here, the first set of inputs are associated with a first probability that the first algorithmic shopping cart is accurate, and the first probability is greater than a threshold. In response to determining to use the first algorithmic shopping cart to process the first transaction, the processor also generates a first receipt based on the first algorithmic shopping cart. The first receipt includes a first set of prices. Each price of the first set of prices corresponds to an item of the first set of items. The processor additionally sends the first receipt to the first person.

According to another embodiment, an apparatus includes a display and a hardware processor. The processor receives a refund request. The refund request includes a request for a refund of a price of an item charged to an account belonging to a person, and information identifying a shopping session of the person in a physical store. In response to receiving the refund request, the processor locates, using the information identifying the shopping session, a video segment of the physical store captured during the shopping session and stored in a database. The processor additionally displays, in a first region of the display, the video segment. The video segment depicts a scenario indicating that the person did not select the item for purchase during the shopping session. The processor further receives information indicating that the person did not select the item for purchase during the shopping session. The information is based at least in part on the video segment. In response to receiving the information indicating that the person did not select the item for purchase during the shopping session, the processor processes the refund request, where processing the refund request includes crediting the account belonging to the person with the price of the item.

According to another embodiment, an apparatus includes a memory and a second hardware processor communicatively coupled to the memory. The memory stores instructions corresponding to a machine learning algorithm. The machine learning algorithm is configured, when implemented by a first hardware processor, to use a set of inputs to select between using an algorithmic shopping cart to process a transaction and using a virtual shopping cart to process the transaction. The set of inputs includes information collected from sensors located in a physical store during a shopping session of a person in the physical store. The transaction is associated with the shopping session of the person. The algorithmic shopping cart includes a first set of items determined by an algorithm, based on the set of inputs, to have been selected by the person during the shopping session. The virtual shopping cart includes a second set of items associated with the shopping session.

The second hardware processor receives feedback for a decision made by the machine learning algorithm. The decision made by the machine learning algorithm is at least one of a decision to use the algorithmic shopping cart to process the transaction, or a decision to use the virtual shopping cart to process the transaction. The feedback indicates either that the algorithmic shopping cart matches the virtual shopping cart, or that the algorithmic shopping cart does not match the virtual shopping cart. The second processor also assigns a reward value to the feedback. The reward value includes at least one of a first positive reward value, a second positive reward value, a first negative reward value, or a second negative reward value. The reward value includes the first positive reward value, when the decision made by the machine learning algorithm includes the decision to use the algorithmic shopping cart to process the transaction and the feedback indicates that the algorithmic shopping cart matches the virtual shopping cart. The reward value includes the second positive reward value, when the decision made by the machine learning algorithm includes the decision to use the virtual shopping cart to process the transaction and the feedback indicates that the algorithmic shopping cart does not match the virtual shopping cart. The reward value includes the first negative reward value, when the decision made by the machine learning algorithm includes the decision to use the algorithmic shopping cart to process the transaction and the feedback indicates that the algorithmic shopping cart does not match the virtual shopping cart. The reward value includes the second negative reward value, when the decision made by the machine learning algorithm comprises the decision to use the virtual shopping cart to process the transaction and the feedback indicates that the algorithmic shopping cart matches the virtual shopping cart. The second processor additionally uses the reward value to update the machine learning algorithm.

According to a further embodiment, an apparatus includes a display and a hardware processor communicatively coupled to the display. The processor displays, in a first region of the display, a virtual shopping cart. The processor also receives information indicating that an algorithm determined that a first physical item was selected by a person during a shopping session in a physical store. The algorithm determined that the first physical item was selected by the person based on a set of inputs received from sensors located within the physical store. In response to receiving the information indicating that the algorithm determined that the first item was selected, the processor displays, in a second region of the display, a first virtual item. The first virtual item includes a graphical representation of the first physical item. The processor additionally displays, in a third region of the display, a first rack video captured during the shopping session of the person in the physical store. The first rack video was captured by a first rack camera of a set of rack cameras located in the physical store. The first rack camera is directed at a first physical rack of a set of physical racks located in the physical store. The first physical rack includes the first physical item. In response to displaying the first rack video, the processor receives information identifying the first virtual item, where the first rack video depicts that the person selected the first physical item while interacting with the first physical rack. In response to receiving the information identifying the first virtual item, the processor stores the first virtual item in the virtual shopping cart.

Certain embodiments provide one or more technical advantages. For example, an embodiment reduces the processing resources spent when reviewing surveillance video of a customer in a store, by presenting multiple camera views of the store at once, synchronized with one another, and configured to capture the shopping session of the customer. As another example, an embodiment provides feedback for a machine learning tracking algorithm, configured to track a customer in a physical store, which may be used to improve the accuracy of the machine learning algorithm. As another example, an embodiment provides an improved graphical user interface that enables a user to view multiple surveillance videos on a single screen and to easily navigate amongst different camera views. As a further example, an embodiment conserves processing resources by using a reinforcement learning algorithm to determine that there is a high probability that certain determinations, made by a machine learning tracking algorithm that is configured to assign items to a customer by tracking the customer in a physical store and identifying those items within the store with which the customer interacts, are correct, such that no additional verification is needed. The system described in the present disclosure may particularly be integrated into a practical application of a remote monitoring system for a physical location, such as a store, where inputs from sensors located in the store may be used to monitor and track events occurring within the store. In particular, the remote monitoring system is capable of automatically analyzing events occurring within the store and identifying those events for which review by an external agent is desirable (because, for example, the events are similar to previous ones in which review was deemed proper). In order to aid the external agent in reviewing an event, the system presents the agent with an improved graphical interface that is designed to display multiple video feeds captured from inside the store during the event on a single screen, and which includes multiple controls to enable the agent to easily navigate the different videos.

Certain embodiments may include none, some, or all of the above technical advantages. One or more other technical advantages may be readily apparent to one skilled in the art form the figures, descriptions, and claims included herein.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present disclosure, reference is now made to the following description, taken in conjunction with the accompanying drawings, in which:

FIGS. 1A and 1B present a comparison between a physical store and a virtual store;

FIGS. 2A and 2B present a comparison between a physical layout of a physical store and a virtual layout of a virtual store;

FIGS. 3A and 3B present a comparison between a physical rack in a physical store and a virtual rack in a virtual store;

FIG. 4 illustrates an example system according to the present disclosure;

FIG. 5A illustrates example locations in a physical store of cameras configured to capture regions of the store for use in the system illustrated in FIG. 4;

FIG. 5B illustrates an example of the regions of a physical store captured by the layout cameras of the system illustrated in FIG. 4;

FIG. 6 illustrates the video processor component of the virtual store tool of the system illustrated in FIG. 4;

FIGS. 7A through 7C present an example illustrating the manner in which the virtual store tool of the system illustrated in FIG. 4 displays camera feed segments associated with the layout cameras and the rack cameras of the system illustrated in FIG. 4;

FIG. 8 presents a flowchart illustrating the process by which the virtual store tool of the system illustrated in FIG. 4 generates and displays camera feed segments associated with the layout cameras and the rack cameras of the system illustrated in FIG. 4;

FIGS. 9A through 9D present examples illustrating the manner in which the virtual store tool of the system illustrated in FIG. 4 may virtually emulate a shopping session occurring in a physical store;

FIG. 10 presents a flowchart illustrating the manner in which the virtual store tool of the system illustrated in FIG. 4 may virtually emulate a shopping session occurring in a physical store;

FIGS. 11A and 11B illustrate an example embodiment of a graphical user interface generated by the virtual store tool of the system illustrated in FIG. 4, which may be used to generate a virtual layout configured to emulate a physical layout of a physical store;

FIG. 12 presents a flowchart illustrating the manner in which the virtual store tool of the system illustrated in FIG. 4 may generate a virtual layout configured to emulate a physical layout of a physical store;

FIGS. 13A and 13B present examples of sensors that may be used to provide input to an algorithm configured to determine items selected by a customer during a shopping session in a physical store;

FIGS. 13C and 13D illustrate an example of the use of sensors coupled to a physical shelf in a physical store to define zones of the physical shelf and its corresponding virtual shelf;

FIG. 14 illustrates a resolution component of the virtual store tool of the system illustrated in FIG. 4;

FIG. 15 illustrates a machine learning component of the virtual store tool of the system illustrated in FIG. 4;

FIG. 16 presents a flowchart illustrating the manner by which the virtual store tool of the system illustrated in FIG. 4 may provide feedback to an algorithm configured to determine the items selected by a customer during a shopping session in a physical store;

FIGS. 17A and 17B present an example illustrating the use of the virtual store tool of the system illustrated in FIG. 4 to virtually emulate a shopping session occurring in a physical store by using suggestions, provided by an algorithm, of products selected during the shopping session;

FIG. 18 presents a flowchart illustrating the use of the virtual store tool of the system illustrated in FIG. 4 to virtually emulate a shopping session occurring in a physical store by using suggestions, provided by an algorithm, of products selected during the shopping session;

FIGS. 19A through 19C present an example illustrating the use of the virtual store tool of the system illustrated in FIG. 4 to process a refund request submitted in response to a prior virtual emulation by the virtual store tool of a shopping session occurring in a physical store;

FIG. 20 presents a flowchart illustrating the use of the virtual store tool of the system illustrated in FIG. 4 to process a refund request submitted in response to a prior virtual emulation by the virtual store tool of a shopping session occurring in a physical store;

FIG. 21 illustrates an example system according to the present disclosure, which includes a virtual store assistant which may be used to identify shopping sessions as candidates for virtual emulation by the virtual store tool;

FIG. 22 illustrates an example process by which a machine learning algorithm, used by the virtual store assistant illustrated in FIG. 21, may decide whether or not a shopping session is a candidate for virtual emulation by the virtual store tool and subsequently receive feedback regarding such decision; and

FIG. 23 presents a flowchart illustrating the use of the virtual store assistant illustrated in FIG. 21 to decide whether or not a shopping session is a candidate for virtual emulation by the virtual store tool and subsequently receive feedback regarding such decision.

DETAILED DESCRIPTION

Embodiments of the present disclosure and its advantages may be understood by referring to FIGS. 1 through 16 of the drawings, like numerals being used for like and corresponding parts of the various drawings. Additional information is disclosed in U.S. patent application Ser. No. 16/663,663 entitled, “Scalable Position Tracking System For Tracking Position In Large Spaces” (attorney docket no. 090278.0176); U.S. patent application Ser. No. 16/663,710 entitled, “Topview Object Tracking Using a Sensor Array” (attorney docket no. 090278.0180); U.S. patent application Ser. No. 16/664,470 entitled, “Customer-Based Video Feed” (attorney docket no. 090278.0187); U.S. patent application Ser. No. 16/664,490 entitled “System and Method for Presenting a Virtual Store Shelf that Emulates a Physical Store Shelf” (attorney docket no. 090278.0188); U.S. patent application Ser. No. 16/938,676 entitled “Feedback and Training for a Machine Learning Algorithm Configured to Determine Customer Purchases During a Shopping Session at a Physical Store” (attorney docket no. 090278.0228), which is a continuation of U.S. patent application Ser. No. 16/794,083 entitled, “Feedback and Training for a Machine Learning Algorithm Configured to Determine Customer Purchases During a Shopping Session at a Physical Store” (attorney docket no. 090278.0210), now U.S. Pat. No. 10,810,428, which is a continuation of U.S. patent application Ser. No. 16/663,564 entitled, “Feedback and Training for a Machine Learning Algorithm Configured to Determine Customer Purchases During a Shopping Session at a Physical Store” (attorney docket no. 090278.0198), now U.S. Pat. No. 10,607,080; U.S. patent application Ser. No. 17/021,011 entitled, “System and Method for Populating a Virtual Shopping Cart Based on Video of a Customer's Shopping Session at a Physical Store” (attorney docket no. 090278.0252), which is a continuation of U.S. patent application Ser. No. 16/663,589 entitled, “System and Method for Populating a Virtual Shopping Cart Based on Video of a Customer's Shopping Session at a Physical Store” (attorney docket no. 090278.0200); and U.S. patent application Ser. No. 16/664,529 entitled, “Tool for Generating a Virtual Store that Emulates a Physical Store” (attorney docket no. 090278.0201), which are all hereby incorporated by reference herein as if reproduced in their entirety.

I. Introduction to Virtual Emulation

This disclosure is generally directed to generating a virtual store that is configured to emulate a physical store, and using the virtual store, along with videos of a shopping session occurring within the physical store, to virtually emulate the physical shopping session. Although this disclosure describes virtual emulation of a physical store, this disclosure contemplates that any type of physical space (e.g., a warehouse, a storage center, an amusement park, an airport, an office building, etc.) may be virtually emulated using the tool described in the present disclosure. For example, the physical store may be a convenience store or a grocery store. This disclosure also contemplates that the physical store may not be a physical building, but a physical space or environment in which shoppers may shop. For example, the physical store may be a grab and go pantry at an airport, a kiosk in an office building, or an outdoor market at a park, etc.

As illustrated in FIG. 1A, a physical store 100 is a brick and mortar store—i.e., a store that is located in a physical building. Customers 105 (who may carry mobile devices 125) enter physical store 100 to purchase items. On the other hand, a virtual store 110 is a computerized representation of a physical store, displayed on a computer or other device 115 belonging to a user 120, as illustrated in FIG. 1B. This disclosure contemplates that user 120 may use virtual store 110 to emulate a shopping session of customer 105 in physical store 100. Virtual store 110 may be generated locally on device 115 or generated remotely and transmitted over a network to device 115.

Virtual store 110 may be configured to emulate physical store 100 in several different ways. For example, in certain embodiments, and as illustrated in FIGS. 2A and 2B, the virtual layout 205 of virtual store 110 is configured to emulate the physical layout 200 of physical store 100. In particular, the shape, location, and orientation of virtual display racks 230 a, 230 b, 230 c, and 230 d are configured to emulate the shape, location, and orientation of physical display racks 210 a, 210 b, 210 c, and 210 d. For example, in the example illustrated in FIG. 2A, physical display racks 210 a and 210 b are located along back wall 235 a of physical layout 200 of physical store 100. Accordingly, virtual display racks 230 a and 230 b are placed along back wall 240 a of virtual layout 205 of virtual store 110, to emulate the location and orientation of physical display racks 210 a and 210 b. Similarly, virtual display rack 230 d is placed along side wall 240 b of virtual layout 205, to emulate the position and orientation of physical display rack 210 d along side wall 235 b, and virtual display rack 230 c is placed in the center of virtual layout 205, to emulate the position and orientation of physical display rack 210 c.

As another example, in some embodiments, the contents of virtual display racks 230 a, 230 b, 230 c, and 230 d are configured to emulate the contents of physical display racks 210 a, 210 b, 210 c, and 210 d. For example, in certain embodiments, virtual display racks 230 a, 230 b, 230 c, and 230 d are each assigned a list of items, wherein the list of items includes those items stored on physical rack 210 a, 210 b, 210 c, and 210 d, respectively. In other embodiments, each virtual display rack is assigned a set of virtual shelves, where the number and placement of the virtual shelves on the virtual display rack are configured to emulate the number and placement of the physical shelves on the corresponding physical display rack. Each virtual shelf of the set of virtual shelves then holds a set of virtual items that is configured to emulate the set of physical items stored on a corresponding physical shelf. Here the virtual items may be configured to emulate the physical items in terms of appearance and/or positioning on the virtual shelf.

As a specific example, FIGS. 3A and 3B present a comparison between physical display rack 210 a and virtual display rack 230 a in one embodiment. As seen in FIG. 3A, physical display rack 210 a includes two physical shelves—first physical shelf 305 a and second physical shelf 305 b. Accordingly, to emulate physical display rack 210 a, virtual display rack 230 a also includes two shelves—first virtual shelf 310 a and second virtual shelf 310 b. Additionally, each of virtual shelves 310 a and 310 b includes a set of virtual items configured to emulate the physical items stored on the corresponding physical shelf of physical shelves 305 a and 305 b. For example, virtual shelf 310 a includes first virtual item 320 a, located in first virtual zone 330 a of virtual shelf 310 a, second virtual item 320 b, located in second virtual zone 330 b of virtual shelf 310 a, and third virtual item 320 c, located in third virtual zone 330 c of virtual shelf 310 a, positioned to emulate the positioning of first physical item 315 a in first physical zone 325 a of physical shelf 305 a, second physical item 315 b in second physical zone 325 b of physical shelf 305 a, and third physical item 315 c in third physical zone 325 c of physical shelf 305 a. Similarly, virtual shelf 310 b includes fourth virtual item 320 d, fifth virtual item 320 e, and sixth virtual item 320 f, positioned, respectively, in fourth virtual zone 330 d, fifth virtual zone 330 e, and sixth virtual zone 330 f of virtual shelf 310 b, to emulate the positioning of fourth physical item 315 d, fifth physical item 315 e, and sixth physical item 315 f in fourth physical zone 325 d, fifth physical zone 325 e, and sixth physical zone 325 f of physical shelf 305 b. Additionally, each of virtual items 320 a through 320 f is configured to emulate the appearance of the corresponding physical item 315 a, 315 b, 315 c, 315 d, 315 e, or 315 f. For example, each virtual item may correspond to a two-dimensional, graphical representation of the corresponding physical item. In this manner, a virtual item may easily be identified based on the appearance of its real world, physical counterpart.

II. System Overview

FIG. 4 illustrates an example system 400 that includes virtual store tool 405, device 115, display 410, network 430 a, network 430 b, layout cameras 490, and rack cameras 495. In certain embodiments, system 400 additionally includes external system 485 and sensors 498. Generally, virtual store tool 405 is configured to generate a virtual store 110 that emulates a physical store 100. In certain embodiments, virtual store tool 405 uses virtual store 110 to generate a receipt for a shopping session conducted by a person 105 in physical store 100, based in part on videos tracking the shopping session, received from layout cameras 490 and/or rack cameras 495 located in the physical store 100. In some embodiments, virtual store tool 405 uses virtual store 110 and videos received from layout cameras 490 and/or rack cameras 495 to process a refund request submitted by a person 105 in response to receiving a receipt from virtual store tool 405 for a shopping session conducted by person 105 in physical store 100. In some embodiments, virtual store tool 405 uses virtual store 110 and videos received from layout cameras 490 and rack cameras 495 to validate a determination made by an algorithm 488 of the items selected by person 105 during the shopping session in physical store 100.

Device 115 includes any appropriate device for communicating with components of system 400 over network 430 a. For example, device 115 may be a telephone, a mobile phone, a computer, a laptop, a wireless or cellular telephone, a tablet, a server, an IoT device, and/or an automated assistant, among others. This disclosure contemplates device 115 being any appropriate device for sending and receiving communications over network 430 a. Device 115 may also include a user interface, such as a microphone, keypad, or other appropriate terminal equipment usable by user 120. In some embodiments, an application executed by a processor of device 115 may perform the functions described herein.

Device 115 may include or be coupled to display 410. Display 410 is a screen used by device 115 to display information received from virtual store tool 405. In certain embodiments, display 410 is a standard display used in a laptop computer. In certain other embodiments, display 410 is an external display device connected to a laptop or desktop computer. In further embodiments, display 410 is a standard touch-screen liquid crystal display found in a typical smartphone or tablet.

As illustrated in FIG. 4, in certain embodiments, display 410 may present camera feed segments 415 a through 415 f, virtual layout 205, virtual rack 230, virtual shopping cart 420, and/or rack camera feed segment 425. Camera feed segments 415 a through 415 f are video recordings of camera feeds received by virtual store tool 405 from layout cameras 490 located in physical store 100, and are assigned to a person 105 conducting a shopping session in physical store 100. The method by which virtual store tool 405 generates camera feed segments 415 a through 415 f and displays camera feed segments 415 a through 415 f on display 410 is described in further detail below, in the discussion of FIGS. 5 through 8.

Virtual layout 205 is assigned to the particular physical store 100 from which virtual store tool 405 received the camera feeds associated with camera feed segments 415 a through 415 f, and is configured to emulate the physical layout 200 of that physical store. The method by which virtual store tool 405 generates virtual layout 205 is described in further detail below, in the discussion of FIGS. 11 and 12.

Virtual rack 230 corresponds to one of the virtual racks included in virtual layout 205 and is configured to emulate a physical rack 210 of physical store 100. Accordingly, virtual rack 230 displays a set of virtual items 320, with each virtual item 320 representing a physical item 315 stored on the corresponding physical rack 210. Virtual shopping cart 420 is used to hold virtual items 320, each of which represents a physical item 315 selected by person 105 during the shopping session in physical store 100. Rack camera feed segment 425 is a recording of a camera feed received by virtual store tool 405 from a rack camera 495. Rack camera 495 is directed at the physical rack 210 of physical store 100 to which virtual rack 230 is assigned. For example, rack camera 495 may be directed at the center of physical rack 210. Virtual shopping cart 420 may be populated by virtual items 320 stored on virtual rack 230, based in part on rack camera feed segment 425. The method by which virtual store tool 405 determines a virtual rack 230 to display on display 410 and then uses virtual rack 230 to populate virtual shopping cart 420 is described in further detail below, in the discussion of FIGS. 9 and 10.

In some embodiments, and as described in further detail below, with respect to FIGS. 11A and 11B, display 410 displays a graphical user interface through which a user 120 may generate a virtual layout 205 configured to emulate a physical layout 200 of a physical store 100. In certain embodiments, and as described in further detail below, with respect to FIGS. 17 and 18, display 410 displays a product list that includes items determined by algorithm 488 to have been selected by person 105 during the shopping session in physical store 100. In some embodiments, and as described in further detail below, with respect to FIGS. 19 and 20, display 410 may display information associated with a request for a refund of the price of one or more products charged to an account of person 105 during a prior shopping session in physical store 100. For example, display 410 may display one or more virtual items 320 which are the subjects of the refund request and/or a textual description of the reason person 105 has submitted the refund request.

Network 430 a facilitates communication between and amongst the various components of system 400 located outside of network 430 b, connecting layout cameras 490, rack cameras 495, and external system 485 to virtual store tool 405. This disclosure contemplates network 430 a being any suitable network that facilitates communication between such components of system 400. Network 430 a may include any interconnecting system capable of transmitting audio, video, signals, data, messages, or any combination of the preceding. Network 430 a may include all or a portion of a public switched telephone network (PSTN), a public or private data network, a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a local, regional, or global communication or computer network, such as the Internet, a wireline or wireless network, an enterprise intranet, or any other suitable communication link, including combinations thereof, operable to facilitate communication between the components.

Network 430 b facilitates communication between and amongst the various components of virtual store tool 405 and layout cameras 490, rack cameras 495, and external system 485. This disclosure contemplates network 430 b being any suitable network that facilitates communication between the components of virtual store tool 405 and layout cameras 490, rack cameras 495, and external system 485. Network 430 b may include any interconnecting system capable of transmitting audio, video, signals, data, messages, or any combination of the preceding. Network 430 b may include all or a portion of a public switched telephone network (PSTN), a public or private data network, a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a local, regional, or global communication or computer network, such as the Internet, a wireline or wireless network, an enterprise intranet, or any other suitable communication link, including combinations thereof, operable to facilitate communication between the components. This disclosure contemplates that network 430 b may be the same network as network 430 a or a separate network from network 430 a.

As seen in FIG. 4, virtual store tool 405 includes a processor 435, a memory 440, and an interface 445. This disclosure contemplates processor 435, memory 440, and interface 445 being configured to perform any of the functions of virtual store tool 405 described herein. Generally, virtual store tool 405 implements layout creator 460, video processor 465, display controller 470, resolution component 475, and machine learning module 480. Virtual store tool 405 may use layout creator 460 to generate a virtual layout 205 configured to emulate a physical layout 200 of a physical store 100. This function of virtual store tool 405 is described in further detail below, in the discussion of FIGS. 11 and 12. Virtual store tool 405 may use video processor 465 to generate camera feed segments 415 and rack camera feed segments 425, assigned to a person 105 conducting a shopping session in physical store 100, based on camera feeds received from layout cameras 490 and rack cameras 495, respectively. This function of virtual store tool 405 is described in further detail below, in the discussion of FIGS. 5 through 8. Virtual store tool 405 may use display controller 470 to adjust the information displayed on display 410, based on input received from device 115. This function of virtual store tool 405 is described in further detail below, in the discussion of FIGS. 7 through 12, and 17 through 21. Virtual store tool 405 may use resolution component 475 to compare the contents of virtual cart 420 to an algorithmic shopping cart, determined by an algorithm 488 to contain items selected by customer 105 during a shopping session in physical store 100. Resolution component 475 may identify any discrepancies between virtual cart 420 and the algorithmic cart, resolve such discrepancies, and generate a receipt to send to customer 105. Resolution component 475 will be described in further detail below, in the discussion of FIG. 14. Finally, virtual store tool 405 may use machine learning module 480 to identify discrepancies between virtual shopping cart 420 and the algorithmic cart and assign metadata to the algorithmic inputs associated with the discrepancies. This metadata may then be used to retrain the algorithm. Machine learning module 480 will be described in further detail below, in the discussion of FIGS. 15 and 16.

Processor 435 is any electronic circuitry, including, but not limited to central processing units (CPUs), graphics processing units (GPUs), microprocessors, application specific integrated circuits (ASIC), application specific instruction set processor (ASIP), and/or state machines, that communicatively couples to memory 440 and controls the operation of virtual store tool 405. Processor 435 may be 8-bit, 16-bit, 32-bit, 64-bit or of any other suitable architecture. Processor 435 may include an arithmetic logic unit (ALU) for performing arithmetic and logic operations, processor registers that supply operands to the ALU and store the results of ALU operations, and a control unit that fetches instructions from memory and executes them by directing the coordinated operations of the ALU, registers and other components. Processor 435 may include other hardware and software that operates to control and process information. Processor 435 executes software stored on memory to perform any of the functions described herein. Processor 435 controls the operation and administration of virtual store tool 405 by processing information received from network 430 a, network 430 b, memory 440, device(s) 115, layout cameras 490, rack cameras 495, and external system 485. Processor 435 may be a programmable logic device, a microcontroller, a microprocessor, any suitable processing device, or any suitable combination of the preceding. Processor 435 is not limited to a single processing device and may encompass multiple processing devices.

Memory 440 may store, either permanently or temporarily, data, operational software, or other information for processor 435. Memory 440 may include any one or a combination of volatile or non-volatile local or remote devices suitable for storing information. For example, memory 440 may include random access memory (RAM), read only memory (ROM), magnetic storage devices, optical storage devices, or any other suitable information storage device or a combination of these devices. The software represents any suitable set of instructions, logic, or code embodied in a computer-readable storage medium. For example, the software may be embodied in memory 440, a disk, a CD, or a flash drive. In particular embodiments, the software may include an application executable by processor 435 to perform one or more of the functions described herein.

Additionally, in certain embodiments, memory 440 may store virtual layouts 205 and sets of videos 450. Each of virtual layouts 205 a through 205 n corresponds to a different physical store 100 and is configured to emulate the physical layout 200 of physical store 100. Virtual layouts 205 may be stored in memory 440 according to a store identification number. In this manner, a given virtual layout 205 a may be retrieved from memory 440 using the store identification number. This disclosure contemplates that set of videos 450 includes the camera feed segments 415 and rack camera feed segments 425 assigned to a given person 105, for example, through identification number 455. Such segments are video recordings of camera feeds received by virtual store tool 405 from layout cameras 490 and rack cameras 495, respectively. For example, set of videos 450 may include camera feed segments 415 a through 415 f and rack camera feed segments 425, assigned to a person 105. The manner in which virtual store tool 405 generates sets of videos 450 is described in further detail below, in the discussion of FIG. 6.

Interface 445 represents any suitable device operable to receive information from networks 430 a and 430 b, transmit information through networks 430 a and 430 b, perform suitable processing of the information, communicate to other devices, or any combination of the preceding. For example, interface 445 receives camera feeds from layout cameras 490 and rack cameras 495. As another example, interface 445 receives input from device 115. Interface 445 represents any port or connection, real or virtual, including any suitable hardware and/or software, including protocol conversion and data processing capabilities, to communicate through a LAN, WAN, or other communication systems that allows virtual store tool 405 to exchange information with device 115, layout cameras 490, rack cameras 495, and/or other components of system 400 via networks 430 a and 430 b.

Database 482 stores videos 484 and shopping session identification information 486. Each video of videos 484 a through 484 h is associated with a layout camera 490 or a rack camera 495 and corresponds to video captured by the associated camera 490/495 extending into the past over a period of time. This period of time may be the entire time cameras 490/495 have been in operation, the past year, the past month, the past week, or any other suitable time period. For example, each of videos 484 a through 484 f may correspond to all of the video captured over the past year by layout cameras 490 a through 490 f, respectively, and each of videos 484 g and 484 h may correspond to all of the video captured over the past year by rack cameras 495 a and 495 b, respectively. Shopping session identification information 486 includes any information that may be used to identify those portions of videos 484 that correspond to a specific shopping session of a specific customer 105 and/or portions of a specific shopping session of a specific customer 105. As an example, for each shopping session conducted in physical store 100, shopping session identification information 486 may include an identification number assigned to the shopping session and a starting and/or ending timestamp, where the starting and/or ending timestamp identifies the location of the shopping session within videos 484. As another example, for each shopping session conducted in physical store 100, shopping session identification information 486 may include an identification number assigned to the shopping session and a set of timestamps and/or time intervals, each of which is associated with an item. Each timestamp/time interval may indicate the approximate time within videos 484 at which it was determined (either by virtual store tool 405 or by algorithm 488) that the item associated with the timestamp/time interval was selected for purchase during the shopping session. In certain embodiments, virtual store tool may use videos 484 and/or shopping session identification information 486 to process requests for refunds associated with previous shopping sessions in physical store 100, as described in further detail below, in the discussion of FIGS. 19 and 20.

External system 485 represents any system operable to receive input from sensors 498 located in physical store 100 and to apply an algorithm 488 to this input to track customers 105 in physical store 100 and/or to determine physical items 315 selected by such customers during shopping sessions in physical store 100. Embodiments of external system 485 are described in U.S. patent application Ser. No. 16/663,710 entitled, “Topview Object Tracking Using a Sensor Array” (attorney docket no. 090278.0180), U.S. Patent Application No. entitled, “Food Detection Using a Sensor Array” (attorney docket no. 090278.0243), and U.S. Patent Application No. entitled, “Self-Serve Beverage Detection and Assignment” (attorney docket no. 090278.0244), the contents of all of which are incorporated by reference herein. This disclosure contemplates that sensors 498 may include any type of suitable sensors, located in physical store 100, and operable to detect customers 105 in physical store 100. For example, physical store 100 may include cameras, light detection and range sensors, millimeter wave sensors, weight sensors, and/or any other appropriate sensors, operable to track a customer 105 in physical store 100 and detect information associated with customer 105 selecting one or more items 315 from physical store 100. This disclosure also contemplates that algorithm(s) 488 may be any suitable algorithm(s) for tracking customers 105 in physical store 100 and determining items 315 selected by customers 105. For example, in certain embodiments, algorithm(s) 488 may be a machine learning algorithm(s).

Layout cameras 490 and rack cameras 495 are located in physical store 100. Each of layout cameras 490 a through 490 f is directed at a location in physical store 100 and captures video and/or images of a region in space around the location. Each of rack cameras 495 is directed at a physical display rack 210 located in physical store 100 and captures video and/or images of the physical display rack 210 and the region in space around the physical display rack 210. This disclosure contemplates that any number of layout cameras 490 may be installed in physical store 100 and connected to virtual store tool 405 through network 430 b. Similarly, any number of rack cameras 495 may be installed in physical store 100 and connected to virtual store tool 405 through network 430 b. For example, in some embodiments, physical store 100 contains the same number of rack cameras 495 as physical shelves 210. In other embodiments, physical store 100 contains more rack cameras 495 than physical shelves 210. In certain embodiments, rack cameras 495 are the same as layout cameras 490. In other embodiments, rack cameras 495 are distinct from layout cameras 490. The operation of layout cameras 490 and rack cameras 495 is described in further detail below, in the discussion of FIGS. 5 and 6.

Modifications, additions, or omissions may be made to the systems described herein without departing from the scope of the invention. For example, system 400 may include any number of users 120, devices 115, displays 410, networks 430 a and 430 b, layout cameras 490, rack cameras 495, and external systems 485. The components may be integrated or separated. Moreover, the operations may be performed by more, fewer, or other components. Additionally, the operations may be performed using any suitable logic comprising software, hardware, and/or other logic.

III. Customer-Based Video Tracking

As described above, virtual store tool 405 may use virtual layout 205 to emulate a shopping session of a customer 105 in a physical store 100 captured by cameras feed segments 415 and/or 425. FIGS. 5 through 8 are used to describe the method by which virtual store tool 405 generates and displays camera feed segments 415 and/or 425.

a. Cameras Used for Customer-Based Video Tracking

FIG. 5A illustrates example locations of layout cameras 490 and rack cameras 495 in a physical store 100. The numbers of layout cameras 490 and rack cameras 495 chosen for a physical store 100 may depend on the size and/or layout of physical store 100. As seen in the example of FIG. 5A, physical store 100 may include five layout cameras 490 a through 490 e. While illustrated as located on the ceiling of physical store 100, this disclosure contemplates that layout cameras 490 may be mounted anywhere in physical store 100. Additionally, in the example of FIG. 5A, physical store 100 may include four rack cameras 495 a through 495 d. While illustrated as located both on the ceiling and sidewalls of physical store 100, this disclosure contemplates that rack cameras 495 may be mounted anywhere in physical store 100. Rack cameras 495 may be separate from layout cameras 490 or the same as layout cameras 490.

Each of rack cameras 495 is directed at a rack 210 located in physical store 100. For example, as illustrated in FIG. 5A, rack camera 495 a is directed at physical display rack 210 a, rack camera 495 b is directed at physical display rack 210 b, rack camera 495 c is directed at physical display rack 210 c, and rack camera 495 d is directed at physical display rack 210 d. While FIG. 5A illustrates a set of five layout cameras 490 and a set of four rack cameras 495 in physical store 100, this disclosure contemplates that any suitable number of layout cameras 490 and rack cameras 495 may be used in physical store 100, depending on the size and/or layout of physical store 100. FIG. 5A additionally illustrates a set of turnstiles 510 located in physical store 100. Turnstiles 510 may be used to control the entry and exit of customers 105 into or out of physical store 100, as described in further detail below, in the discussion of FIG. 6.

As illustrated in FIG. 5B, each of layout cameras 490 is directed at a particular location in physical store 100 and captures a region 505 of the layout 200 of physical store 100, surrounding the location. For example, first layout camera 490 a is directed at a first location and captures video and/or images of a first region 505 a of physical store 100; second layout camera 490 b is directed at a second location and captures video and/or images of a second region 505 b of physical store 100; third layout camera 490 c is directed at a third location and captures video and/or images of a third region 505 c of physical store 100; fourth layout camera 490 d is directed at a fourth location and captures video and/or images of a fourth region 505 d of physical store 100; and fifth layout camera 490 e is directed at a fifth location and captures video and/or images of a fifth region 505 e of physical store 100. In certain embodiments, layout cameras 490 may capture overlapping regions of physical store 100. For example, as illustrated in FIG. 5B, all of third region 505 c is overlapped by portions of first region 505 a, second region 505 b, fourth region 505 d, and fifth region 505 e. The overlapping regions of physical store 100 may be a result of the proximity of layout cameras 490 to one another. Generally, by capturing overlapping regions of physical store 100, certain portions of physical layout 200 can be captured by multiple layout cameras 490. This may be desirable, to provide sufficient camera coverage of physical layout 200 in the event that certain of layout cameras 490 malfunction or go offline.

While illustrated in FIG. 5B as rectangular in shape, this disclosure contemplates that regions 505 may be of any shape or size. For example, in certain embodiments, regions 505 are elliptical in shape. In some embodiments, regions 505 are of uniform size and shape. For example, as illustrated in FIG. 5B, regions 505 a through 505 e are all the same shape and size. In other embodiments, regions 505 may include regions 505 of different sizes and shapes.

b. Camera Feed Processing

The videos and/or images of physical store 100 captured by layout cameras 490 and/or rack cameras 495 are transmitted to virtual store tool 405 in the form of camera feeds. Virtual store tool 405 then uses video processor 465 to generate camera feed segments 415 and rack camera feed segments 425, assigned to a person 105 conducting a shopping session in physical store 100, based on these camera feeds. FIG. 6 illustrates the operation of video processor 465 of virtual store tool 405.

FIG. 6 presents an example of the operation of video processor 465 of virtual store tool 405, in an embodiment that includes a first layout camera 490 a, a second layout camera 490 b, and a rack camera 495 a. As illustrated in FIG. 6, video processor 465 receives first camera feed 605 a from first layout camera 490 a, second camera feed 605 b from second layout camera 490 b, and rack camera feed 620 a from rack camera 495 a. In certain embodiments, video processor 465 receives first camera feed 605 a, second camera feed 605 b, and rack camera feed 620 a directly from layout cameras 490 a, 490 b, and rack camera 495 a. In some embodiments, video processor 465 receives first camera feed 605 a, second camera feed 605 b, and rack camera feed 620 a from interface 445.

Prior to processing camera feeds 605 a, 605 b, and 620 a, video processor 465 first determines that a person 105, associated with an identification number 455, entered physical store 100. This disclosure contemplates that video processor 465 may determine that person 105 entered physical store 100 in any suitable manner. For example, in certain embodiments, physical store 100 includes turnstiles 510, which control the entry of persons 105 into the store. A turnstile 510 may open upon person 105 scanning a QR code, located on a physical card or a mobile device 125 belonging to person 105, using a scanner 515 attached to the turnstile 510. Accordingly, the scanning of the QR code may generate a notification, sent to virtual store tool 405, indicating that person 105 entered physical store 100. As another example, in some embodiments, an algorithm 488 may be used to determine that person 105 entered physical store 100, based on information received from sensors 498 located in physical store 100. An example of such an algorithm 488 will be described in further detail below, in the discussion of FIGS. 13 through 16.

This disclosure contemplates that camera feeds 605 and 620 are synchronized in terms of timestamps, such that video associated with a given timestamp from each of camera feeds 605 a, 605 b, and 620 a corresponds to the same real time within physical store 100. Such synchronization may be achieved in any suitable manner. For example, in certain embodiments, layout cameras 490 and rack cameras 495 are plugged into the same ethernet switch. Determining that person 105 entered physical store 100 may then include receiving a starting timestamp 610 corresponding to the timestamp at which person 105 entered physical store 100.

Given that data packets associated with first camera feed 605 a, second camera feed 605 b, and rack camera feed 620 a may arrive at virtual store tool 405 over network 430 b at different times, this disclosure contemplates that rather than virtual store tool 405 streaming first camera feed 605 a, second camera feed 605 b, and rack camera feed 620 a from starting timestamp 610 onwards, video processor 465 of virtual layout tool 405 stores recordings of first camera feed 605 a, second camera feed 605 b, and rack camera feed 620 a, lasting a predefined amount of time, in memory 440. Such recordings may then be replayed, each synchronized with the others according to timestamps. Accordingly, once video processor 465 determines starting timestamp 610, corresponding to the timestamp at which person 105 entered physical store 100, video processor 465 next prepares segments of each camera feed, starting at starting timestamp 610 and ending at ending timestamp 615. Video processor 465 then stores these segments in memory 440. For example, video processor 465 prepares first camera feed segment 415 a, corresponding to a recording of first camera feed 605 a from starting timestamp 610 to ending timestamp 615, second camera feed segment 415 b, corresponding to a recording of second camera feed 605 b from starting timestamp 610 to ending timestamp 615, and rack camera feed segment 425 a, corresponding to a recording of rack camera feed 620 a from starting timestamp 610 to ending timestamp 615. Video processor 465 then stores each of segments 415 a, 415 b, and 425 a in memory 450.

This disclosure contemplates that the time interval between starting timestamp 610 and ending timestamp 615 may be any predetermined amount of time. For example, in certain embodiments, the time interval is five minutes. In order to capture video of a shopping session lasting more than this predetermined amount of time, once camera feeds 605 a, 605 b, and 620 a reach ending timestamp 615, video processor 465 may store additional recordings of camera feeds 605 a, 605 b, and 620 a, starting at ending timestamp 615 and ending at a new ending timestamp, the new ending timestamp occurring at the predetermined amount of time after ending timestamp 615. Video processor 465 may store any number of additional camera feed segments in memory 440, each corresponding to an additional predetermined interval of time. In certain embodiments, video processor 465 continues to record such additional camera feed segments until it receives an indication that person 105 has left physical store 100.

Video processor 465 may store camera feed segments 415 and 425 for any number of persons 105. Accordingly, video processor 465 may store a collection of camera feed segments 415 and 425 assigned to a person 105 as set of videos 450, where set of videos 450 is assigned identification number 455 associated with person 105. As an example, a first person 105 a may enter physical store 100 at a first starting timestamp 610 a and a second person 105 b may enter physical store 100 at a second starting timestamp 610 b after the first starting timestamp 610 a, wherein the second starting timestamp 610 b is within the predefined time interval after first starting timestamp 610 a, such that the camera feed segments recorded for first person 105 a will contain video that overlaps with the camera feed segments recorded for second person 105 b. Accordingly, video processor 465 may store the camera feed segments recorded for first person 105 a, along with an identification number 455 a, assigned to first person 105 a, in memory 440, as set of videos 450 a. Similarly, video processor 465 may store the camera feed segments recorded for second person 105 b, along with an identification number 455 b, assigned to second person 105 b, in memory 440, as set of videos 450 b. Virtual store tool 405 may then retrieve from memory 440 the camera feed segments associated with a given person 105, using the identification number 455 assigned to that person.

Video processor 465 may be a software module stored in memory 440 and executed by processor 435. An example of the operation of video processor 465 is as follows: (1) receive camera feeds 605 and 620 from cameras 490 and 495, respectively; (2) determine that a person 105 entered physical store 100; (3) determine the timestamp 610 corresponding to the time at which person 105 entered physical store 100; (4) record camera feed segments 415 and 425 from camera feeds 605 and 620, respectively, where the camera feed segments correspond to recordings of camera feeds 605 and 620 from timestamp 610, corresponding to the time at which person 105 entered physical store 100, and lasting a predetermined amount of time to ending timestamp 615; and (5) store camera feed segments 415 and 425 in memory 440 according to an identification number 455 of person 105, as set of videos 450.

c. Displaying Camera Feed Segments

Once video processor 465 has recorded set of videos 450 from camera feeds 605 and 620, virtual store tool 405 may then use display controller 470 to display set of videos 450 on display 410 of device 115. In certain embodiments, virtual store tool 405 may display set of videos 450 on display 410 of device 115 in the form of a graphical user interface 700. FIGS. 7A through 7C present an example illustrating the manner in which virtual store tool 405 displays set of videos 450 on display 410.

FIG. 7A illustrates an embodiment in which virtual store tool 405 instructs display 410 to display four camera feed segments 415 a through 415 d. Virtual store tool 405 displays first camera feed segment 415 a in a first region 702 a of display 410, second camera feed segment 415 b in a second region 702 b of display 410, third camera feed segment 415 c in a third region 702 c of display 410, and fourth camera feed segment 415 d in a fourth region 702d of display 410. Virtual store tool 405 may instruct display 410 to display any number of camera feed segments 415. For example, in certain embodiments, virtual display tool 405 may instruct display 410 to display the same number of camera feed segments 415 as stored in set of videos 450. In some embodiments, virtual display tool 405 may instruct display 410 to display fewer camera feed segments 415 than stored in set of videos 450. This may be desirable in embodiments in which physical store 100 is a large store that includes a large number of layout cameras 490. In such embodiments, displaying all of camera feed segments 415 on display 410 may make it difficult for a user 120 to view specific features of physical store 100 in any one of the displayed camera feed segments 415. Accordingly, virtual store tool 405 may display a subset of camera feed segments 415 on display 410. Virtual store tool 405 may select a subset of camera feed segments 415 to display on display 410 in any suitable manner. As an example, in certain embodiments, virtual store tool 405 may display a subset of camera feed segments 415 that includes, at any given time, those camera feed segments 415 capturing regions of physical store 100 closest to the location of person 105, to whom set of videos 450 is assigned. In such embodiments, when set of videos 450 depicts person 105 moving to a new location in physical store 100, virtual store tool 405 may replace the subset of camera feed segments 415 currently displayed on display 410 with a new subset of camera feed segments 415, which includes those camera feed segments 415 that capture regions of physical store 100 closest to the new location of person 105. Virtual store tool 405 may determine the subset of camera feed segments 415 that capture regions of physical store 100 closest to the location or person 105 in any suitable manner. For example, in certain embodiments, virtual store tool 405 may receive an indication of the location of person 105 from a machine-learning algorithm 488 configured to track the locations of a person 105 in physical store 100, based on inputs received from a set of sensors 498 located in physical store 100.

As illustrated in FIG. 7A, in addition to displaying camera feed segments 415, virtual store tool 405 also assigns a slider bar 705 to set of videos 450 and displays copies of slider bar 705 along with each camera feed segment 415. For example, virtual store tool 405 displays a first copy 705 a of slider bar 705 along with first camera feed segment 415 a, a second copy 705 b of slider bar 705 along with second camera feed segment 415 b, a third copy 705 c of slider bar 705 along with third camera feed segment 415 c, and a fourth copy 705 d of slider bar 705 along with fourth camera feed segment 415 d. Each copy of slider bar 705 may contain a slider 710 configured to control the playback progress of the associated camera feed segment 415. For example, the position of slider 710 on slider bar 705 indicates the current playback progress of the associated camera feed segment 415. The position of slider 710 may be manually adjusted (e.g., by a user 120) to a new position corresponding to a new playback time. Such adjustment may result in the playback of the associated camera feed segment adjusting to the new playback time.

In certain embodiments, the playback of each camera feed segment 415 is synchronized with that of the other camera feed segments 415, such that an adjustment of the slider 710 on any of the copies of slider bar 705 leads to a corresponding adjustment of the playback progress of all of the displayed camera feed segments 415. For example, if slider 710 is adjusted on first copy 705 a of slider bar 705 from a first playback time to a second playback time, slider 710 on second copy 705 b of slider bar 705, slider 710 on third copy 705 c of slider bar 705, and slider 710 on fourth copy 705 d of slider bar 705 will all similarly adjust from the first playback time to the second playback time. This may be desirable for a user 120 using camera feed segments 415 to observe a shopping session of a customer 105 in physical store 100. User 120 may adjust the playback progress of camera feed segments 415 until user 120 determines that camera feed segments 415 have reached a point of interest to user 120, rather than viewing the entire, uninterrupted playback of camera feed segments 415.

In certain embodiments, slider bar 705 may include one or more markers 715. For example, as illustrated in FIG. 7A, slider bar 705 may include a first marker 715 a, located at a first marker position on slider bar 705 and corresponding to a first marker playback time, as well as a second marker 715 b, located at a second marker position on slider bar 705 and corresponding to a second marker playback time. First marker 715 a is associated with a first event occurring at the first marker playback time and second marker 715 b is associated with a second event occurring at the second marker playback time. The first event and the second event may include any type of events occurring within physical store 100. For example, the first event may be associated with a person 105 a selecting a physical item 315 a from a physical shelf 305 a located in a physical rack 210 a in physical store 100. Similarly, the second event may be associated with person 105 a selecting a second physical item 315 b from a second physical shelf 305 b located in a second physical rack 210 b in physical store 100.

The locations for first marker 715 a and second marker 715 b on slider bar 705 may be determined in any suitable manner. As an example, in certain embodiments, the first event, associated with first marker 715 a, and the second event, associated with second marker 715 b, may be determined by an algorithm 488, based on a set of inputs received from sensors 498 located within physical store 100. For example, algorithm 488 may determine that the first event takes place at a first time, corresponding to a first timestamp, and that the second event takes place at a second time, corresponding to a second timestamp. Virtual store tool 405 may then use the first and second timestamps to place first marker 715 a and second marker 715 b on slider bar 705, at positions corresponding to the timestamps. An example algorithm 488, used to determine the timing of the first and second events, is described in further detail below, in the discussion of FIGS. 13 through 16. The use of markers 715 may be desirable for a user 120 using camera feed segments 415 to observe a shopping session of customer 105 in physical store 100. Rather than viewing the entire, uninterrupted playback of camera feed segments 415, user 120 may adjust the playback progress of camera feed segments 415 until slider 710 reaches one of the events associated with first marker 715 a or second marker 715 b, to, for example, observe customer 105 selecting a physical item 315 from a physical rack 210 in physical store 100.

As described above, in the discussion of FIG. 6, each of camera feed segments 415 is of a predetermined time interval, lasting from a starting timestamp 610 to an ending timestamp 615. Accordingly, in certain embodiments in which customer 105 remains within physical store 100 for longer than the predetermined time interval, multiple camera feed segments may exist, from each of layout cameras 490. For example, virtual store tool 405 may store in memory 440 camera feed segments 415 for a first time interval, a second time interval, a third time interval, and a fourth time interval. Memory 440 stores any number of camera feed segments 415 for any number of time intervals. In such embodiments, when slider 710 reaches the end of slider bar 705, virtual store tool 405 may replace those camera feed segments 415 currently displayed on display 410, with the next set of camera feed segments 415, corresponding to the time interval immediately following the time interval captured by the currently displayed set of camera feed segments 415. This process of replacing the currently displayed camera feed segments 415 with a new set of camera feed segments 415, corresponding to the time interval immediately following the time interval captured by the currently displayed set of camera feed segments 415 may continue until virtual store tool 405 determines that customer 105 has left physical store 100.

Virtual store tool 405 may determine that customer 105 has left physical store 100 in any suitable manner. As an example, in certain embodiments, virtual store tool 405 may determine that customer 105 has left physical store 100 based on input received from user 120. For example, in embodiments in which set of videos 450 are displayed on display 410 in the form of a graphical user interface 700, the graphical user interface 700 may include an interactive button 730 (e.g., an exit customer button) through which user 120 may indicate that he/she observed customer 105 exiting physical store 100, on camera feed segments 415, as illustrated in FIG. 7B. As another example, virtual store tool 405 may determine that customer 105 has left physical store 100 based on information received from an algorithm 488 configured to track customers 105 within physical store 100. Such as algorithm 488 is described in further detail below, in the discussion of FIGS. 13 through 16. As a further example, virtual store tool 405 may determine that customer 105 has left physical store 100 based on information received from physical store 100. For example, physical store 100 may include a set of turnstiles 510 near the exit of physical store 100. In order to open a turnstile 510 and leave physical store 100, a customer 105 may be asked to scan the same QR code that he/she used to enter physical store 100. Scanning the QR code may then send a signal to virtual store tool 405, indicating that customer 105 has exited physical store 100.

In certain embodiments, a user 120 may request that virtual store tool 405 display video segments 415/425 that include video of physical store 100 from (1) before customer 105 entered physical store 100 and/or (2) after virtual store tool 405 determined that customer 105 exited physical store 100. As a specific example of a situation in which this may arise, user 120 may view a second customer 105 b handing an item to a first customer 105 a, while viewing camera segments 415 a through 415 f associated with the first customer 105 a. User 120 may not, however, have a clear view of which physical item 315 second customer 105 a handed to first customer 105 b. Accordingly, user 120 may request that virtual store tool 405 display video of physical store 100 leading up to the time interval captured by camera segments 415 a through 415 f, in order to determine which physical item 315 that second customer 105 b may have selected from physical store 100 to hand to first customer 105 a. In certain embodiments, in response to receiving a request from user 120 to display video segments 415/425 that include video of physical store 100 from before customer 105 entered physical store 100 and/or after customer 105 exited physical store 100, virtual store tool 405 may access videos 484 stored in database 482. As an example, in response to receiving a request to generate video segments 415/425 that capture video of physical store 100 before customer 105 entered the store, video processor 465 may identify the starting timestamp 610 associated with customer 105′s shopping session, and locate this starting timestamp 610 within videos 484 a through 484 h. Video processor 465 may then store segments of each of videos 484 a through 484 h, beginning at a timestamp corresponding to a set time interval before starting timestamp 610 and lasting until starting timestamp 610, as video segments 415/425. Video processor 465 may then display such segments 415/425 on graphical user interface 700. As another example, in response to receiving a request to generate video segments 415/425 that capture video of physical store 100 after customer 105 exited the store, video processor 465 may identify the ending timestamp 615 identified by virtual store tool 405 as corresponding to when customer 105 exited physical store 100, and locate this ending timestamp 615 within videos 484 a through 484 h. Video processor 465 may then store segments of each of videos 484 a through 484 h, beginning at ending timestamp 615 and lasting until a timestamp corresponding to a set time interval after ending timestamp 615, as video segments 415/425. Video processor 465 may then display such segments 415/425 on graphical user interface 700.

In certain embodiments, in order to assist a user 120 in determining which of camera feed segments 415 may include information of interest to the user, virtual store tool 405 is configured to highlight certain camera feed segments 415, at certain times, based on events depicted in those camera feed segments 415, at those certain times. For example, as illustrated in FIG. 7B, virtual store tool 405 may be configured to determine that a given camera feed segment 415 a depicts customer 105 at a first time. Accordingly, virtual store tool 405 may highlight camera feed segment 415 a in response to determining that slider 710 on slider bar 705 reached that first time. Here, highlighting camera feed segment 415 a may include any manner by which virtual store tool 405 may draw attention toward camera feed segment 415 a. For example, as illustrated in FIG. 7B, highlighting camera feed segment 415 a may include placing a frame 720 around camera feed segment 415 a. As another example, highlighting camera feed segment 415 a may include increasing the size of camera feed segment 415 a, depicted on display 410, relative to the other camera feed segments 415.

In certain embodiments, the graphical user interface 700 displayed on display 410 may be used by a user 120 to monitor a shopping session of a customer 105 a in physical store 100. To aid such a user 120 in monitoring a particular customer 105 a in a physical store that includes several other customers 105, virtual store tool 405 may additionally display an image 725 of customer 105 a, captured when customer 105 a entered physical store 100. For example, in certain embodiments in which physical store 100 includes turnstiles 510 to control the entry of persons 105 into the store, physical store 100 may include a camera configured to take an image 725 of customer 105 a as customer 105 a passes through a turnstile 510.

In certain embodiments in which slider bar 705 includes one or more markers 715, each marker 715 may include metadata 740 describing the event associated with the marker 715. An example of one such embodiment is illustrated in FIG. 7C. As described above, in the discussion of FIG. 7A, each marker 715 a and 715 b may be associated with an event consisting of customer 105 a selecting a physical item 315 from a physical shelf 305 of a physical rack 210 located in physical store 100. Accordingly, each marker may include metadata 740 indicating an identification number 745 assigned to the physical item 315 selected by customer 105 a, an identification number 750 assigned to the physical shelf 305 from which customer 105 a selected the physical item 315, and/or an identification number 755 assigned to the physical rack 210 that includes the physical shelf 305 from which customer 105 a selected the physical item 315. In certain embodiments, item identification number 745 may correspond to a zone identification number 745, identifying a zone of physical shelf 305 from which customer 105 a selected the physical item 315. The use of shelf zones will be described in further detail below, in the discussion of FIGS. 13C and 13D.

Virtual store tool 405 may use metadata 740 in any suitable manner. For example, in certain embodiments, when slider 710 on slider bar 705 reaches first marker 715 a, virtual store tool 405 may use metadata 740 to determine that customer 105 selected a physical item 315 from physical rack 210. Accordingly, virtual store tool 405 may display rack camera segment 425 a on display 410, where rack camera segment 425 a depicts video of physical rack 210. Rack camera segment 425 a may be synchronized with camera feed segments 415 a through 415 d, such that an adjustment of the slider 710 on any of the copies of slider bar 705 leads to a corresponding adjustment of the playback progress of rack camera segment 425 a. Automatically displaying rack camera segment 425 a, in response to slider 710 reaching marker 715 on slider bar 705 may be desirable, to provide a user 120 with a view of physical rack 210 through which user 120 is able to observe customer 105 selecting a physical item 315 from physical rack 210. In certain embodiments, user 120 may be able to use a second graphical user interface to choose a rack camera 495 from among several potential rack cameras 495 to assign to physical rack 210, to provide user 120 with a rack camera segment 425 a that displays the best view of physical rack 210, as determined by user 120. This aspect of virtual store tool 405 will be described in further detail below, in the discussion of FIGS. 11 and 12.

FIG. 8 presents a flowchart illustrating the process by which virtual store tool 405 generates camera feed segments 415 and 425 and displays such segments on display 410. In step 805, virtual store tool 405 receives a set of layout camera feeds 605 from a set of layout cameras 490 and a set of and rack camera feeds 620 from a set of rack cameras 495 located in physical store 100. In step 810, virtual store tool 405 determines whether a person 105 entered physical store 100. This disclosure contemplates that virtual store tool 405 may determine that person 105 entered physical store 100 in any suitable manner. For example, in certain embodiments, physical store 100 includes turnstiles 510, which control the entry of persons 105 into the store. A turnstile 510 may be opened upon person 105 scanning a QR code, located on a physical card or a mobile device 125 belonging to person 105. Accordingly, the scanning of the QR code may generate a notification, sent to virtual store tool 405, to indicate that person 105 entered physical store 100. As another example, in some embodiments, an algorithm 488 may be used to determine that person 105 entered physical store 100, based on information received from sensors 498 located in physical store 100.

If, in step 810, virtual store tool 405 determines that person 105 entered physical store 100, in step 815, virtual store tool 405 stores a set of camera feed segments 415 and 425 in memory 440. Each camera feed segment of camera feed segments 415 corresponds to a recording of one of the camera feeds 605 from a starting timestamp 610 to an ending timestamp 615. Similarly, each rack camera feed segment of rack camera feed segments 425 corresponds to a recording of one of the rack camera feeds 620 from starting timestamp 610 to ending timestamp 615. Starting timestamp 610 corresponds to the time at which person 105 entered physical store 100. Ending timestamp 615 corresponds to a predetermined time interval after starting timestamp 610.

In step 820, virtual store tool 405 assigns copies of a slider bar 705 to each camera feed segment 415 and 425. Slider 710 on each copy of slider bar 705 moves forward as the corresponding camera feed segment 415 and/or 425 progresses. In certain embodiments, the copies of slider bar 705 are synchronized with one another such that all of camera feed segments 415 and 425 progress together, at the same pace. Additionally, in such embodiments, an adjustment of the slider 710 on any of the copies of slider bar 705 leads to a corresponding adjustment of the playback progress of all of camera feed segments 415 and 425. This may be desirable for a user 120 using camera feed segments 415 to observe a shopping session of a customer 105 in physical store 100. User 120 may adjust the playback progress of camera feed segments 415 until user 120 determines that camera feed segments 415 have reached a point of interest to user 120, rather than viewing the entire, uninterrupted playback of camera feed segments 415.

In step 825, virtual store tool 405 presents one or more camera feed segments 415 and/or 425 on display 410, along with corresponding copies of slider bar 705. For example, virtual store tool 405 may display first camera feed segment 415 a, along with first copy 705 a of slider bar 705 in a first region of display 410, second camera feed segment 415 b, along with second copy 705 b of slider bar 705 in a second region of display 410, third camera feed segment 415 c, along with third copy 705 c of slider bar 705 in a third region of display 410, and fourth camera feed segment 415 d, along with fourth copy 705 d of slider bar 705 in a fourth region of display 410. Virtual store tool 405 additionally plays camera feed segments 415 and/or 425, such that slider 710 on each copy of slider bar 705 progresses.

In step 830, virtual store tool 405 next determines whether an adjustment occurred for any slider 710 in a copy of slider bar 705,from a first position on slider bar 705 to a second position on slider bar 705, where the first position corresponds to a first playback time and the second position corresponds to a second playback time. If, in step 830, virtual store tool 405 determines that an adjustment occurred, virtual store tool 405 next adjusts the playback progress of each of camera feed segments 415 and 425 from the first playback time to the second playback time.

In step 840, virtual store tool 405 determines whether person 105 has left physical store 100. Virtual store tool 405 may determine that customer 105 has left physical store 100 in any suitable manner. As an example, in certain embodiments, virtual store tool 405 may determine that customer 105 has left physical store 100 based on input received from user 120. For example, in embodiments in which camera feed segments 415 and/or 425 are displayed on display 410 in the form of a graphical user interface 700, the graphical user interface 700 may include an interactive button 730 (e.g., an exit customer button) through which user 120 may indicate that he/she observed customer 105 exiting physical store 100 on one or more camera feed segments 415. As another example, virtual store tool 405 may determine that customer 105 has left physical store 100 based on information received from an algorithm 488 configured to track customers 105 within physical store 100. Such as algorithm 488 is described in further detail below, in the discussion of FIGS. 13 through 16. As a further example, virtual store tool 405 may determine that customer 105 has left physical store 100 based on information received from physical store 100. For example, physical store 100 may include a set of turnstiles 510 near the exit of physical store 100. In order to open a turnstile 510 and leave physical store 100, a customer 105 may be asked to scan the same QR code that he/she used to enter physical store 100. Scanning the QR code may then send a signal to virtual store tool 405, indicating that customer 105 has exited physical store 100.

If, in step 840, virtual store tool 405 determines that person 105 has not left physical store 100, in step 845, virtual store tool 405 determines whether camera feed segments 415 and 425 have reached ending timestamp 615. If, in step 845, virtual store tool 405 determines that camera feed segments 415 and 425 have not reached ending timestamp 615, virtual store tool returns to step 830, to determine whether an adjustment occurred for any slider 710 in a copy of slider bar 705, from a first position on slider bar 705 to a second position on slider bar 705. On the other hand, if, in step 845, virtual store tool 405 determines that camera feed segments 415 and 425 have reached ending timestamp 615, virtual store tool 405 returns to step 825 and displays a new set of camera feed segments 415 and/or 425 on display 410, where the new set of camera feed segments corresponds to recordings of camera feeds 605 and/or 620 over a time interval immediately following the previous time interval associated with the previous set of camera feed segments 415 and/or 425.

Modifications, additions, or omissions may be made to method 800 depicted in FIG. 8. Method 800 may include more, fewer, or other steps. For example, steps may be performed in parallel or in any suitable order. While discussed as virtual store tool 405 (or components thereof) performing the steps, any suitable component of system 400, such as device(s) 115 for example, may perform one or more steps of the method.

IV. Virtual Emulation of a Shopping Session

As described above, camera feed segments 415 and 425 may be used in conjunction with virtual layout 205 in order to virtually emulate a shopping session occurring in physical store 100 and captured by camera feed segments 415 and/or 425. For example, in certain embodiments, camera feed segments 415 and 425, along with virtual layout 205, may be presented to a user 120, in the form of a graphical user interface 700. Here, camera feed segments 415 and 425 may be assigned to a customer 105 and capture a shopping session of customer 105 in physical store 100. User 120 may monitor camera feed segments 415 and 425 to view customer 120 selecting physical items 315 from physical racks 210. Accordingly, user 120 may populate a virtual shopping cart 420 with virtual items 320 that represent the physical items 315 selected by customer 105, such that at the end of customer 105′s shopping session, virtual shopping cart 420 may include a virtual item 320 for each physical item 315 selected by customer 105.

FIGS. 9A through 9D present further examples of a graphical user interface 700, displayed on display 410, that may be used to virtually emulate a shopping session occurring in physical store 100 and captured by camera feed segments 415 and 425. As illustrated in FIG. 9A, virtual store tool 405 may display camera feed segments 415 in a first region 955 of display 410, as described above in the discussion of FIGS. 7A through 7C. Virtual store tool 405 may additionally display virtual layout 205 in a second region 960 of display 410. Virtual layout 205 is configured to emulate the physical layout 200 of physical store 100. As illustrated in FIG. 9A, virtual layout 205 includes a set of virtual racks 230. This disclosure contemplates that virtual layout 205 may include any number of virtual racks 230, where the number of virtual racks 230 displayed on virtual layout 205 corresponds to the number of physical racks 210 in physical store 100. The layout of virtual racks 230 in virtual layout 205 is configured to emulate the arrangement of the corresponding physical racks 210 in physical store 100.

a. Receiving an Indication of an Event

As illustrated in FIG. 9B, virtual store tool 405 may receive an indication of an event associated with a physical rack 210 a located in physical store 100. In certain embodiments, the event associated with physical rack 210 a may include customer 105 interacting with physical rack 210 a. For example, the event associated with physical rack 210 a may include customer 105 a approaching physical rack 210 a, and/or selecting a physical item 315 f from physical rack 210 a. The indication of the event may include any suitable indication received by virtual store tool 405. For example, in certain embodiments, the indication of the event may include user 120 selecting virtual shelf 230 a in virtual layout 205, in response to viewing customer 105 approaching and/or interacting with physical rack 210 a. As another example, the indication of the event may include slider 710 on slider bar 705 reaching a marker 715, where the marker 715 indicates the physical rack 210 associated with the event, through metadata 740. As a further example, in certain embodiments, the indication of the event may include receiving information from an algorithm 488 configured to determine that customer 105 approached and/or selected an item 315 from physical rack 210 a, based on inputs received from sensors 498 located in physical store 100.

In certain embodiments, in which the graphical user interface 700 displayed on display 410 may be used by a user 120 to monitor a shopping session of a customer 105 in physical store 100, virtual store tool 405 may display a predicted location 950 of customer 105 on virtual layout 205, based on the current playback progress of camera feed segments 415 and/or 425. Predicted location 950 may correspond to the probable location of customer 105 in physical layout 200, as determined by an algorithm 488 configured to track customers 105 in physical store 100, based on inputs received from sensors 498 located in physical store 100, at a physical time corresponding to the current playback progress of camera feed segments 415 and/or 425. This may aid a user 120 in monitoring a particular customer 105 a in a physical store that includes several other customers 105. While illustrated in FIG. 9B as dot 950 on virtual layout 205, the predicted location of customer 105 may be presented on virtual layout 205 in any suitable manner. For example, the predicted location may be a line, including the predicted path of customer 105. In such embodiments, the indication of the event may include user 120 selecting virtual shelf 230 a in virtual layout 205, in response to viewing customer 105 approaching and/or interacting with physical rack 210 a and/or viewing predicted location 950 of customer 105 on virtual layout 205 indicating customer 105's proximity to physical rack 210 a.

In response to receiving the indication of the event, virtual store tool 405 may display the virtual rack 230 a corresponding to the physical rack 210 a associated with the event, in a third region 905 of display 410, where virtual rack 230 a is configured to emulate physical rack 210 a. In certain embodiments, third region 905 of display 410 may be located to the right of virtual layout 205. In certain embodiments, virtual store tool 405 may additionally highlight virtual rack 230 a, in virtual layout 205, in response to receiving the indication of the event associated with physical rack 210 a. Highlighting virtual rack 230 a may include any method of distinguishing virtual rack 230 a from the other virtual racks 230 b through 230 k. For example, as illustrated in FIG. 9B, highlighting virtual rack 230 a may include placing a frame around virtual rack 230 a. Highlighting virtual rack 230 a may additionally include applying a color to virtual rack 230 a, and/or any other suitable method of distinguishing virtual rack 230 a from the remaining virtual racks 230 b through 230 k.

As illustrated in FIG. 9B, virtual rack 230 a, displayed in third region 905 of display 410 includes a set of virtual items 320 a through 320 h. Virtual items 320 a through 320 h are configured to emulate the physical items stored on physical rack 210 a. In certain embodiments, virtual items 320 a through 320 h are displayed in third region 905 as a list of items, where the names of the items in the list correspond to the names of the physical items 315 a through 315 h stored on physical rack 210 a. In other embodiments, the appearance of virtual rack 230 a, displayed in third region 905, is configured to emulate the appearance of physical rack 210 a. For example, first virtual shelf 310 a is configured to emulate first physical shelf 305 a, second virtual shelf 310 b is configured to emulate second physical shelf 305 b, and third virtual shelf 310 c is configured to emulate third physical shelf 305 c. In particular, first virtual item 320 a is located in a first zone 330 a of first virtual shelf 310 a to emulate the location of first physical item 315 a in a first zone 325 a of first physical shelf 305 a. Similarly, second virtual item 320 b is located in a second zone 330 b of first virtual shelf 310 a, to the right of first virtual item 320 a, to emulate the location of second physical item 315 b in a second zone 325 b of first physical shelf 305 a, and third virtual item 320 c is located in a third zone 330 c of first virtual shelf 310 a, to the right of second virtual item 320 b, to emulate the location of third physical item 315 c in a third zone 325 c of first physical shelf 305 a. Virtual items 320 d through 320 f are similarly located on second virtual shelf 310 b to emulate the locations of the physical items 315 d through 315 f, located on second physical shelf 305 b, and virtual items 320 g and 320 h are located on third virtual shelf 310 c to emulate the locations of physical items 315 g and 315 h located on third physical shelf 305 c. To further emulate physical items 315, each of virtual items 320 may include a graphical representation of the corresponding physical item 315.

In addition to displaying virtual rack 230 a in region 905 of display 410, in response to receiving the indication of the event associated with physical rack 210 a, virtual store tool 405 may also display rack camera segment 425 a in a fourth region 970 of display 410, as illustrated in FIG. 9C. In certain embodiments, the fourth region 970 of display 410 is to the right of third region 905. Rack camera segment 425 a depicts physical rack 210 a, during the time interval in which the event occurs. For example, in embodiments in which the event includes customer 105 approaching physical rack 210 a, rack camera segment 425 a depicts customer 105 approaching physical rack 210 a. As another example, in embodiments in which the event includes customer 105 selecting an item 315 f from physical rack 210 a, rack camera segment 425 a depicts customer 105 selecting item 315 f from physical rack 210 a.

Rack camera segment 425 a may be synchronized with camera feed segments 415 a through 415 f, such that an adjustment of the slider 710 on any of the copies of slider bar 705 leads to a corresponding adjustment of the playback progress of rack camera segment 425 a. Displaying rack camera segment 425 a, in response to receiving the indication of the event may be desirable, to provide a user 120 with a view of physical rack 210 a through which user 120 is able to observer customer 105 approaching and/or interacting with physical rack 210 a. For example, rack camera segment 425 a may help user 120 to see if customer 105 selected an item 315 from physical rack 210 a. User 120 may then use this information to populate virtual cart 420, as described in further detail below, in the discussion of FIG. 9D. In certain embodiments, user 120 may be able to select a rack camera 495 to assign to physical rack 210 to provide user 120 with a rack camera segment 425 a that displays the best view of physical rack 210 a, as determined by user 120. This aspect of virtual store tool 405 will be described in further detail below, in the discussion of FIGS. 11 and 12.

b. Receiving Information Identifying a Selected Item

In certain embodiments in which the event includes person 105 selecting an item from physical shelf 210 a, the indication of the event may include information identifying the item selected by person 105. For example, if the event includes person 105 selecting physical item 315 f from physical rack 210 a, the indication of the event received by virtual store tool 405 may include information identifying physical item 315 f and/or virtual item 320 f. As an example, in certain embodiments, each physical shelf 305 of physical rack 210 a includes a set of weight sensors 1300, coupled to zones 325 of the physical shelf 305, as described below, in the discussion of FIGS. 13B through 13D. When person 105 removes an item 315 from physical shelf 305, the weight sensor 1300 coupled to the zone 325 of physical shelf 305 on which the item 315 is located may send information to virtual store tool 405 (either directly, or through other components of system 400, such as external system 485), indicating that the item 315 has been selected from physical shelf 305 of physical rack 210 a. Virtual store tool 405 may use this information to highlight the corresponding virtual item 320 on virtual rack 230 a, displayed in third region 905 of display 410. For example, a weight sensor coupled to a third zone of second physical shelf 305 b of physical rack 210 a may send information to virtual store tool 405 indicating that item 315 f has been removed from the third zone of second physical shelf 305 b of physical rack 210 a.

As another example, in certain embodiments, the indication of the event may include slider 710 on slider bar 705 reaching a marker 715. Markers 715 may include metadata 740, as described above, in the discussion of FIG. 7C. Metadata 740 may include information indicating an identification number 745 assigned to the physical item 315 selected by customer 105, an identification number 750 assigned to the physical shelf 305 from which customer 105 selected the physical item 315, and/or an identification number 755 assigned to the physical rack 210 that includes the physical shelf 305 from which customer 105 selected the physical item 315. When, for example, slider 710 on slider bar 705 reaches marker 715 a, virtual store tool 405 may read metadata 740 assigned to marker 715 a, to identify that person 105 selected physical item 315 f from second physical shelf 305 b of physical rack 210 a. Markers 715 may be added to slider bar 705 in any suitable manner. For example, in certain embodiments, virtual display tool 405 adds markers 715 to slider bar 705 based on information received from an algorithm 488 configured to track customers 105 in physical store 100 and to determine the physical items 315 selected by each customer 105, based on inputs received from sensors 498 located in physical store 100.

In response to receiving information identifying physical item 315 f as being the physical item selected by person 105 from physical rack 210 a, virtual store tool 405 may highlight sixth virtual item 320 f, located on second virtual shelf 310 b of virtual rack 230 a. Highlighting sixth virtual item 320 f may include any method of distinguishing sixth virtual item 320 f from the remaining virtual items 320. For example, highlighting sixth virtual item 320 f may include placing a frame around sixth virtual item 320 f, as illustrated in

FIG. 9C, enlarging sixth virtual item 320 f compared to the other virtual items 320, and/or any other suitable method of distinguishing sixth virtual item 320 f from the remaining virtual items 320.

c. Populating a Virtual Cart

In certain embodiments, the graphical user interface 700 displayed by virtual store tool 405 on display 410 may additionally include a virtual shopping cart 420, as illustrated in FIG. 9D. Virtual shopping cart 420 may be used to further emulate a shopping session of a customer 105 in physical store 100, by storing virtual items 320 corresponding to the physical items 315 selected by person 105 during his/her shopping session. Virtual store tool 405 may display virtual shopping cart 420 in a fifth region 965 of display 410. In certain embodiments, the fifth region 965 of display 410 is located between virtual rack 230 b, displayed in third region 905 of display 410, and rack camera segment 425 a.

In certain such embodiments, receiving information identifying physical item 315 f as being the physical item selected by person 105 from physical rack 210 a, may include receiving information associated with dragging and dropping virtual item 320 f, corresponding to physical item 315 f, from virtual rack 230 a, displayed in region 905, to virtual shopping cart 420. For example, a user 120 may observe customer 105 selecting physical item 315 f on camera feeds segments 415 a through 415 f and/or rack camera feed segment 425 a. Accordingly, user 120 may select virtual item 320 f from virtual rack 230 a, where virtual item 320 f corresponds to physical item 315 f and is configured to emulate physical item 315 f. User 120 may then drag virtual item 320 f to virtual shopping cart 420 and drop virtual item 320 f in virtual shopping cart 420. In order to help aid user 120 in observing customer 105 selecting a physical item 315 on camera feed segments 415 a through 415 f and/or rack camera feed segment 425 a, in certain embodiments, user 120 can make any of the displayed camera feed segments 415 a through 415 f and/or rack camera feed segment 425 a larger than the others, by selecting the camera feed segments 415 a through 415 f and/or rack camera feed segment 425 a. For example, user 120 can click on a given camera feed segment 415 or 425, to instruct virtual store tool 405 to increase the size of the segment presented on display 410.

In response to receiving information identifying physical item 315 f as the physical item selected by person 105 from physical rack 210 a—either from metadata 740, weight sensors 1300 coupled to physical shelf 305 b, a dragging and dropping of virtual item 320 f into virtual shopping cart 420, and/or any other suitable method of receiving information identifying physical item 315 f—virtual store tool 405 may store virtual item 320 f, corresponding to physical item 315 f, in virtual shopping cart 420. Virtual shopping cart 420 may store any number of virtual items 320. For example, as the playback of camera feed segments 415 and 425 progresses, virtual store tool 405 may receive further information identifying an additional, different physical item 315 as having been selected by person 105 from a physical rack 210. Physical rack 210 may be the same as physical rack 210 a or different from physical rack 210 a. In response to receiving the information identifying the additional physical item 315, virtual store tool 405 may store an additional virtual item 320, corresponding to the additional physical item 315, in virtual shopping cart 420. This process may repeat any number of times, such as a number of times corresponding to the number of times the camera feed segments 415 and 425 indicate that a person 105 selected a physical item 315 from a physical rack 210.

As illustrated in FIG. 9D, in certain embodiments, virtual shopping cart 420 may display each virtual item 320 as a graphical representation of the corresponding physical item 315 and/or a textual description 910 of the corresponding physical item 315. Virtual shopping cart 420 may also indicate a quantity 915 of each virtual item 320 f contained in the virtual shopping cart 420. For example, virtual shopping cart 420 may indicate a quantity 915 of two virtual items 320 f, to emulate the fact that customer 105 selected two physical items 315 f from physical rack 210 a. Quantity 915 of each virtual item 320 may be increased in any suitable manner. For example, in certain embodiments, quantity 915 of virtual item 320 f may be increased by dragging and dropping virtual item 320 f, corresponding to physical item 315 f, from virtual rack 230 a, displayed in region 905, to virtual shopping cart 420 multiple times. As another example, in some embodiments, quantity 915 of virtual item 320 f may be increased by a user 120 interacting with graphical user interface 700 through an addition button 925. Similarly, quantity 915 of virtual item 320 f may be decreased by user 120 interacting with graphical user interface 700 through a subtraction button 925. User 120 may also remove virtual item 320 f from virtual shopping cart 420 by interacting with graphical user interface 700 through a trash button 930.

At the end of the shopping session of customer 105 in physical store 100 (i.e., when virtual store tool 405 determines that customer 105 has exited physical store 100), virtual shopping cart 420 may be used to charge customer 105 for physical items 315 selected by customer 105 during his/her shopping session, and to send a receipt to customer 105. Additionally, virtual shopping cart 420 may be used to validate a determination made by an algorithm 488, based on inputs received from sensors 498 located in physical store 100, of the physical items 315 selected by customer 105 during his/her shopping session. These aspects of virtual store tool 405 will be described in further detail below, in the discussion of FIGS. 13 through 16.

d. Method for Virtually Emulating a Physical Shopping Session

FIG. 10 presents a flowchart illustrating the manner in which virtual store tool 405 emulates a shopping session of a customer 105 in a physical store 100, using virtual layout 205 and camera feed segments 415 and/or 425 received from physical store 100, and capturing the shopping session. In step 1005, virtual store tool 405 displays virtual layout 205 of virtual store 110. Virtual layout 205 is configured to emulate a physical layout 200 of physical store 100. In particular, the arrangement of virtual racks 230 on virtual layout 205 is configured to emulate the physical layout 200 of physical racks 210 in physical store 100.

In step 1010, virtual store tool 405 determines whether the tool has received an indication of an event associated with a person 105 interacting with a physical rack 210 of physical store 100, during a shopping session in physical store 100. This event may include customer 105 approaching a physical rack 210 and/or selecting a physical item 315 from physical rack 210. The indication of the event may include any suitable information that indicates that customer 105 interacted with physical rack 210. For example, in certain embodiments, the indication of the event may include user 120 selecting virtual shelf 230 in virtual layout 205, in response to viewing customer 105 approaching and/or selecting physical item 315 from physical rack 210 on a set of camera feed segments 415, generated from camera feeds 605 received from layout cameras 490, located in physical store 100 and capturing the shopping session of customer 105. As another example, in certain embodiments, the indication of the event may include slider 710 on slider bar 705, assigned to camera feed segments 415, reaching a marker 715. Marker 715 may include metadata 740 indicating the physical rack 210 associated with the event. As a further example, the indication of the event may include receiving information from an algorithm 488 configured to determine that customer 105 approached and/or selected an item 315 from physical rack 210, based on inputs received from sensors 498 located in physical store 100.

If, in step 1010, virtual store tool 405 receives an indication of an event associated with person 105 interacting with physical rack 210, in step 1015, virtual store tool 405 displays the virtual rack 230 corresponding to physical rack 210 (i.e., configured to emulate physical rack 210), in region 905 of display 410. Additionally, in step 1020, virtual store tool 405 displays a rack camera segment 425 generated from a rack camera feed 620 received from a rack camera 495 assigned to physical rack 210. Rack camera segment 425 depicts physical rack 210 during the time interval in which the event occurs.

In step 1025, virtual store tool 405 determines whether the tool has received information identifying a first virtual item 320. As an example, in certain embodiments, each physical shelf 305 of physical rack 210 includes a set of weight sensors 1300, coupled to zones of the physical shelf 305, as described below, in the discussion of FIGS. 13B through 13D. When person 105 removes an item 315 from physical shelf 305, the weight sensor 1300 coupled to the zone of physical shelf 305 on which the item 315 is located may send information to virtual store tool 405 (either directly, or through other components of system 400, such as external system 485), indicating that the item 315 has been selected from physical shelf 305 of physical rack 210 a. As another example, in certain embodiments, the indication of the event may include slider 710 on slider bar 705 reaching marker 715 a or 715 b. Markers 715 a and 715 b may include metadata 740, as described above, in the discussion of FIG. 7C. Metadata 740 may include information indicating an identification number 745 assigned to the physical item 315 selected by customer 105, an identification number 750 assigned to the physical shelf 305 from which customer 105 selected the physical item 315, and/or an identification number 755 assigned to the physical rack 210 that includes the physical shelf 305 from which customer 105 selected the physical item 315. Accordingly, when slider 710 on slider bar 705 reaches a marker 715, virtual store tool 405 may receive information identifying physical item 315, by reading metadata 740 assigned to marker 715, to identify that person 105 selected physical item 315 from physical shelf 305 of physical rack 210. Markers 715 may be added to slider bar 705 in any suitable manner. For example, in certain embodiments, virtual display tool 405 adds markers 715 to slider bar 705 based on information received from an algorithm 488 configured to track customers 105 in physical store 100 and to determine the physical items 315 selected by each customer 105, based on inputs received from sensors 498 located in physical store 100. As a further example, receiving information identifying physical item 315/virtual item 320 may include receiving information associated with dragging and dropping virtual item 320, configured to emulate physical item 315, from virtual rack 230, displayed in region 905 of display 410, to virtual shopping cart 420.

If, in step 1025, virtual store tool 405 determines that the tool has received information identifying first virtual item 320/physical item 315, in step 1030, virtual store tool 405 stores first virtual item 320 in virtual shopping cart 420. In step 1035, virtual store tool 405 determines whether the shopping session of customer 105 has ended (i.e., whether customer 105 has left physical store 100). Virtual store tool 405 may determine that customer 105 has left physical store 100 in any suitable manner. As an example, in certain embodiments, virtual store tool 405 may determine that customer 105 has left physical store 100 based on input received from user 120. For example, in embodiments in which camera feed segments 415 and/or 425 are displayed on a graphical user interface 700 on display 410, graphical user interface 700 may additionally include an interactive button 730 (e.g., an exit customer button) through which user 120 may indicate that he/she observed customer 105 exiting physical store 100, on one or more of camera feed segments 415 and/or 425. As another example, virtual store tool 405 may determine that customer 105 has left physical store 100 based on information received from an algorithm 488 configured to track customers 105 within physical store 100. As a further example, virtual store tool 405 may determine that customer 105 has left physical store 100 based on information received from physical store 100. For example, physical store 100 may include a set of turnstiles 510 located near the exit of physical store 100. In order to open a turnstile 510 and leave physical store 100, a customer 105 may be asked to scan the same QR code that he/she used to enter physical store 100. Scanning the QR code may then send a signal to virtual store tool 405, indicating that customer 105 has exited physical store 100. In certain embodiments, in response to determining that customer 105 has left physical store 100, virtual store tool 105 sends a notification to a device 125 of customer 105, indicating that customer 105 should expect to receive a receipt for his/her shopping session in physical store 105 within a set time period.

If, in step 1035, virtual store tool 405 determines that the shopping session of customer 105 in physical store 100 has not ended, virtual store tool 405 returns to step 1010, to determine whether customer 105 has selected any additional items 315 from physical racks 210. Specifically, virtual store tool 405 determines whether the tool has received an indication of an event associated with customer 105 interacting with another physical rack 210. Physical rack 210 may be the same or a different physical rack from the physical rack with which virtual store tool 405 previously determined that customer 105 interacted. In this manner, virtual store tool 405 may populate virtual cart 420 with any number of virtual items 320.

On the other hand, if, in step 1035, virtual store tool 405 determines that the shopping session has ended, then, in step 1040, virtual store tool 405 charges customer 105 for the items 315 selected by customer 105 during the shopping session, based on the virtual items 320 stored in virtual cart 420, and generates a receipt. The manner in which virtual store tool 405 generates the receipt is described in further detail below, in the discussion of FIG. 14. In order to charge customer 105, this disclosure contemplates that virtual store tool 405 may store payment information for customer 105, according to an identification number 455 assigned to customer 105, in memory 440. Next, in step 1045, virtual store tool 405 sends the receipt to customer 105.

Modifications, additions, or omissions may be made to method 1000 depicted in FIG. 10. Method 1000 may include more, fewer, or other steps. For example, steps may be performed in parallel or in any suitable order. While discussed as virtual store tool 405 (or components thereof) performing the steps, any suitable component of system 400, such as device(s) 115 for example, may perform one or more steps of the method.

V. Virtual Layout Creation

In certain embodiments, layout creator 460 of virtual store tool 405 is configured to display a second graphical user interface 1100 through which a user 120 may generate a virtual layout 205 configured to emulate a physical layout 200 of a physical store 100. FIGS. 11A and 11B illustrate an example embodiment of such a graphical user interface 1100.

a. Placing Virtual Racks on Virtual Layout to Emulate the Physical Layout of Physical Racks

Layout creator 460 of virtual store tool 405 may generate a virtual layout 205 configured to emulate a physical layout 200 of a physical store, in response to receiving a set of positions and orientations associated with physical racks 210 located in physical store 100. Layout creator 460 may receive the set of positions and orientations in any suitable manner. For example, virtual store tool 405 may receive the positions and orientations from user 120, through graphical interface 1100, by user 120 creating virtual racks 230 on graphical interface 1100 and then dragging and dropping the virtual racks 230 to given positions on virtual layout 205 and/or rotating virtual racks 230 to given orientations on virtual layout 205. As another example, layout creator 460 may receive the positions and orientations from a file uploaded to virtual store tool 405. For example, user 120 may upload a file including the positions and orientations using the “drop your file here” button 1150 on graphical user interface 1100. The file may include a list including pairs of positions and angles. In certain embodiments, each position may specify the center of mass position of a physical shelf 210 in physical store 100. In some embodiments, each position may specify the position of a given corner of a physical shelf 210 in physical store 100. The positions may be specified in terms of any coordinate system superimposed on physical layout 200. For example, each position may be specified as an (x,y) coordinate of a Cartesian coordinate system with an origin located in the middle of physical store 100. In certain embodiments, each orientation may specify the angle of a physical shelf 210 relative to a given direction. For example, each orientation may specify the angle of a physical shelf 210 relative to the x-axis of the Cartesian coordinate system of the previous example. In certain embodiments, for each physical shelf 210, the file may additionally include a length and width of the physical shelf 210.

In response to receiving the positions and orientations, layout creator 460 places each virtual rack 230 at a virtual position and with a virtual orientation on virtual layout 205. Here, the virtual position and the virtual orientation for a given virtual rack 230 on virtual layout 205 represents the physical location and the physical orientation of the corresponding physical rack 210 in physical layout 200. While FIG. 11A illustrates an example including eleven virtual racks 230, this disclosure contemplates that virtual layout 205 may include any number of virtual racks 230. Additionally, while FIG. 11A illustrates racks 230 a through 230 k separated from one another on virtual layout 205, in certain embodiments, two or more racks may be touching one another. For example, as illustrated in FIG. 17B, a pair of racks 230 m and 230 may be positioned back-to-back on virtual layout 205. Similarly, as illustrated in FIG. 17B, a pair of racks 230 b and 230 c may be positioned side-by-side on virtual layout 205. In certain embodiments, virtual store tool stores the resulting virtual layout 205 in memory 440, according to a store identification number 1105. Additionally, layout creator 460 may store each virtual rack 230 of virtual layout 205 in memory 440 according to a rack identification number 755.

Virtual layout tool 405 may also modify a given virtual layout 205, in response to receiving a new position and/or orientation for any of virtual shelves 230 on virtual layout 205. Modifying virtual layout 205 may be desirable in situations in which the physical layout 200 emulated by virtual layout 205 has changed. Layout creator 460 may receive new positions and/or new orientations for virtual shelves 230 in any suitable manner. For example, layout creator 460 may read the new positions and/or orientations from a file. The file may specify a new position and/or orientation for a virtual rack 230 a using the identification number 755 a assigned to virtual rack 230 a. For example, for each virtual rack 230, the file may include the identification number 755 assigned to the virtual rack 230, Cartesian coordinates (x,y) of the new position for the rack, and an angle measured relative to the x-axis, specifying the new orientation for the rack. As another example, layout creator 460 may receive a new positions and/or orientation for a virtual rack 230, based on input received from graphical user interface 1100. For example, as illustrated in FIG. 11A, layout creator 460 may receive input representing a dragging of virtual rack 230 k from a first position on virtual layout 205 to a new position 1130 on virtual layout 205. In response to receiving such input, layout creator 460 may place virtual rack 230 k at the new virtual position 1130, as illustrated in FIG. 11B. As another example, virtual store tool 205 may receive input representing a rotation of virtual rack 230 from a first orientation to a new orientation. In response to receiving such input, layout creator 460 may place virtual rack 230 on virtual layout 205 with this new orientation.

b. Placing Virtual Items on Virtual Racks to Emulate the Physical Items Located on Physical Racks

In addition to placing virtual racks 230 on virtual layout 205, layout creator 460 is operable to populate virtual racks 230 with virtual items 320. For example, layout creator 460 may receive a planogram specifying the physical items 315 to be placed on each physical rack 210 in physical store 100. For example, for each physical rack 210, the planogram may include a list of physical items 315 to be placed on the physical rack 210. For each physical item 315, the list may specify the shelf 305 of physical rack 210 on which the physical item 315 is to be placed, as well as the zone 325 of each shelf 305 on which the physical item 315 is to be placed. In response to receiving the planogram, layout creator 460 may place corresponding virtual items 320 on virtual racks 230. As another example, layout creator 460 may receive a list of virtual items 320 for each virtual rack 230, with each virtual item 320 in the list associated with a physical item 315. Such a list may specify a store identification number 1105, a rack identification number 755, a shelf identification number 750, and/or a zone identification number 745 for each virtual item 320 emulating a physical item 315. Here, store identification number 1105 identifies a physical store 100 storing physical item 315, rack identification number 755 identifies a physical rack 210 in physical store 100 holding physical item 315, shelf identification number 750 identifies a physical shelf 305 of physical rack 210, on which physical item 315 is placed, and zone identification number 745 identifies a zone of physical shelf 305 housing physical item 315. In certain embodiments, zone identification number 745 may correspond to a sensor identification number of a sensor 498 coupled to the zone of physical shelf 305 housing physical item 315. Layout creator 460 may then store the virtual item 320 in memory 440 according to store identification number 1105, rack identification number 755, shelf identification number 750, and zone identification number 745, where layout creator 460 has assigned store identification number 1105 to virtual layout 205, rack identification number 755 to virtual rack 230, shelf identification number 750 to virtual shelf 310, and zone identification number 745 to a virtual zone of virtual 310 configured to emulate the physical zone of physical shelf 305 housing physical item 315. The division of physical shelves 305 and virtual shelves 310 into zones is described in further detail below, in the discussion of FIGS. 13C and 13D.

As another example, layout creator 460 may receive virtual items 320 to store on a given virtual rack 230 from a drop-down-menu that includes a scrollable list of items. An example of such a drop-down-menu 1135 is illustrated in FIG. 11B. As illustrated in FIGS. 11A and 11B, user 120 may select a physical item name 1130 from drop-down-menu 1135 for a given virtual shelf 230. In response, layout creator 460 may store the virtual item 320 associated with the physical item 315 having physical item name 1130 in virtual shelf 230.

Second graphical user interface 1100 may also be used to assign rack cameras 495 to each of virtual racks 230 in virtual layout 205. As illustrated in FIGS. 11A and 11B, layout creator 460 may present a set of rack camera feed segments 425 a through 425 f to user 120, through second graphical user interface 1100. Each rack camera feed segment 425 is generated from a rack camera feed 620 received from a rack camera 495 located in physical store 100. In certain embodiments, a user 120 may select a rack camera 495 to assign to a virtual rack 230. User 120 may select a given rack camera 495 based on which of rack camera feed segments 425 a through 425 f provides user 120 with the best view of physical rack 210 (emulated by virtual rack 230), as determined by user 120. User 120 may select rack camera 495 in any suitable manner. As an example, in certain embodiments, user 120 may assign a given rack camera 495 to virtual rack 230 by clicking on the rack camera segment 425 generated by rack camera 495 and displayed on second graphical user interface 1100. For example, user 120 may click on rack camera segment 425 a to assign rack camera 495 a, which generated rack camera segment 425 a, to virtual rack 230 a. In response to user 120 clicking on rack camera segment 425 a, layout creator 460 may associate a rack camera identification number 1125 a, assigned to rack camera 495 a, with virtual rack 230 a.

Second graphical user interface 1100 may be used to generate any number of virtual layouts 205, which layout creator 460 may store in memory 440 according to store identification numbers 1105. Virtual store tool 405 may later retrieve a given virtual layout 205 using the associated store identification number 1105 and display the virtual layout 205 on display 410.

Layout creator 460 may be a software module stored in memory 440 and executed by processor 435. An example of the operation of layout creator 460 is as follows: (1) receive a set of positions and orientations associated with physical racks 210 located in physical store 100; (2) for each received position and orientation, place a virtual rack 230 on virtual layout 205, at a virtual position and with a virtual orientation representing the physical position and physical orientation of the corresponding physical rack 210 in physical layout 200; (3) if input is received, associated with a new position for a virtual rack 230, place virtual rack 230 at the new position on virtual layout 205; (4) if input is received, associated with a new orientation for a virtual rack 230, place virtual rack 230 on virtual layout 205, with the new orientation; (5) for each virtual rack 230, receive a set of virtual items 320, and place the set of virtual items 320 on virtual rack 230; (6) for each virtual rack 230, assign a rack camera 495 to the virtual rack.

c. Method for Generating a Virtual Layout

FIG. 12 presents a flowchart illustrating the manner in which virtual store tool 405 may generate a virtual layout 205 configured to emulate a physical layout 200 of a physical store 100. In step 1205, virtual store tool 405 places a set of virtual racks 230 at virtual positions and with virtual orientations on virtual layout 205, where the virtual positions and the virtual orientations of virtual racks 230 are chosen to emulate the physical positions and physical orientations of physical racks 210 in physical store 100. Virtual store tool 405 may receive the virtual positions and virtual orientations in any suitable manner. For example, virtual store tool 405 may receive the positions and orientation from user 120 through graphical user interface 1100. As another example, virtual store tool 405 may receive the positions and orientations from a file uploaded to virtual store tool 405.

In step 1210, virtual store tool 405 determines whether input representing a dragging of a virtual rack 230 to a new virtual position was received. If, in step 1210, virtual store tool 405 determines that input representing a dragging of a virtual rack 230 to a new virtual position was received, in step 1215, virtual store tool 405 places virtual rack 230 at the new virtual position and proceeds to step 1220. On the other hand, if, in step 1210, virtual store tool 405 does not determine that input representing a dragging of a virtual rack 230 to a new virtual position was received, virtual store tool 405 simply proceeds to step 1220.

In step 1220, virtual store tool 405 determines whether input representing a rotation of a virtual rack 230 from an initial orientation to a new orientation was received. If, in step 1220, virtual store tool 405 determines that input representing a rotation of a virtual rack 230 from an initial orientation to a new orientation was received, virtual store tool 405 adjusts the orientation of the virtual rack 230 from the initial orientation to the new orientation, in step 1225, and proceeds to step 1230. On the other hand, if, in step 1220, virtual store tool 405 determines that input representing a rotation of a virtual rack 230 from an initial orientation to a new orientation was not received, virtual store tool 405 proceeds to step 1230.

In step 1230, virtual store tool 405 receives, for each virtual rack 230, a set of virtual items 320 assigned to the virtual rack 230. Virtual store tool 405 may receive the sets of virtual items 320 in any suitable manner. For example, virtual store tool 405 may receive a planogram, specifying the physical items 315 to be placed on each physical rack 210 in physical store 100. For example, for each physical rack 210, the planogram may include a list of physical items 315 to be placed on the physical rack. For each physical item 315, the list may specify the shelf 305 of physical rack 210 on which the physical item 315 is to be placed, as well as the zone 325 of each shelf 305 on which the physical item 315 is to be placed. As another example, virtual store tool 405 may receive virtual items 320 from a drop-down-menu 1135 displayed on display 410. The drop-down-menu 1135 may include a list of physical items 315 from which a user 120 may select one or more items to be placed on each virtual rack 230. Drop-down-menu 1135 may include a scrollable list of any number of physical items 315. In response to receiving a selection of a physical item 315 from drop-down-menu 1135, virtual store tool 405 may identify the corresponding virtual item 320. After virtual store tool 405 has received the sets of virtual items 320, in step 1235, virtual store tool 405 places each set of virtual items 320 on the corresponding virtual rack 230.

In step 1240, virtual store tool 405 assigns a rack camera 495 to each virtual rack 230. Virtual store tool 405 may assign a rack camera 495 to each virtual rack 230 in any suitable manner. For example, in certain embodiments, a user 120 may select rack cameras 495 to assign to virtual racks 230. User 120 may select a given rack camera 495 for a virtual rack 230 based on which of rack camera feed segments 425 a through 425 f provides user 120 with the best view of the corresponding physical rack 210, as determined by user 120. In step 1245, virtual store tool 405 stores virtual layout 205 in memory 440. In certain embodiments, virtual store tool 405 may store virtual layout 205 in memory 440 according to a store identification number 1105.

Modifications, additions, or omissions may be made to method 1200 depicted in FIG. 12. Method 1200 may include more, fewer, or other steps. For example, steps may be performed in parallel or in any suitable order. While discussed as virtual store tool 405 (or components thereof) performing the steps, any suitable component of system 400, such as device(s) 115 for example, may perform one or more steps of the method.

VI. Use in Conjunction with an External Algorithm Configured to Track Customers in the Physical Store

Virtual store tool 405 may be used in conjunction with an algorithm 488, generated by external system 485, and configured to track customers 105 and to determine items 315 selected by a given customer 105 a during a shopping session of customer 105 a in physical store 100, based on inputs received from sensors 498 located in physical store 100. For example, virtual store tool 405 may be used to validate the determinations made by algorithm 488 and/or to help improve the accuracy of algorithm 488. FIGS. 13 through 16 are used to describe this aspect of virtual store tool 405.

a. Algorithm Input Sensors

As described above, external algorithm 488 is configured to track customers 105 and to determine items selected by a customer 105 during a shopping session in physical store 100, based on inputs received from sensors 498 located in physical store 100. This disclosure contemplates that physical store 100 may include any type of suitable sensors 498. For example, physical store 100 may include cameras, light detection and range sensors, millimeter wave sensors, weight sensors, and/or any other appropriate sensors, operable to track a customer 105 in physical store 100 and detect information associated with customer 105 selecting one or more items from physical store 100.

FIGS. 13A through 13D present examples of an embodiment in which physical store 100 includes both cameras 1305 and weight sensors 1300 for sensors 498. This disclosure contemplates that external system 485 may process position information received from the cameras 1305, and weight information received from the weight sensors 1300, using an algorithm 488, to determine which customers 105 removed which items from physical display racks 210 located in physical store 100. In this manner, external system 485 may generate an algorithmic shopping cart of items determined by the algorithm 488 to have been selected by a customer 105, during a shopping session in physical store 100.

As seen in FIG. 13A, the interior of physical store 100 may include an array of cameras 1305 positioned on the ceiling of store 100. In certain embodiments, this array of cameras 1305 may include layout cameras 490 and/or rack cameras 495. In other embodiments, the array of cameras 1305 is separate from layout cameras 490 and rack cameras 495. Generally, the array of cameras 1305 produces videos of portions of the interior of physical store 100. These videos may include frames or images of customers 105 within the space. External system 485 processes these frames from array of cameras 1305 to detect customers 105 within the frames.

As illustrated in FIG. 13A, the array of cameras 1305 may include cameras 1305 arranged in a grid pattern across the ceiling of physical store 100. Although this disclosure shows the array of cameras 1305 including fifty cameras 1305, the array of cameras 1305 may include any suitable number of cameras 1305. Generally, cameras 1305 in the array of cameras 1305 are arranged to form a rectangular array. In the example of FIG. 13A, the array of cameras 1305 is a 5 x 10 array of cameras 1305 (e.g., five rows and ten columns of cameras 1305). The array of cameras 1305 may be arranged in an array of any suitable dimensions.

Each camera 1305 is communicatively coupled to external system 485 and communicates captured video to external system 485. Cameras 1305 are communicatively coupled to external system 485 in any suitable manner. For example, cameras 1305 may be hardwired to components of external system 485. As another example, cameras 1305 may wirelessly couple to external system 485 using any suitable wireless protocol (e.g., WiFi).

Cameras 1305 may be any suitable devices for capturing videos of the interior space of physical store 100. For example, cameras 1305 may be three-dimensional cameras that can capture two-dimensional video of the space (e.g., x-y plane) and also detect the heights of people and/or objects in the video. As another example, cameras 1305 may be two-dimensional cameras that capture two-dimensional videos of the space. The array of cameras 1305 may include a mixture of different types of cameras 1305.

FIG. 13B presents an example weight sensor 1300 that may be coupled to a shelf 305 of a physical rack 210 of physical store 100 to detect the weight of items 315 positioned on the shelf 305. Weight sensor 1300 may then communicate this information to external system 485. External system 485 tracks the weights detected by weight sensors 1300 to determine if, and when, items 315 are removed from the physical rack 210.

As seen in FIG. 13B, weight sensor 1300 includes plates 1315 a and 1315 b, load cells 1310 a, 1310 b, 1310 c, and 1310 d, and wires 1320 a, 1320 b, 1320 c, 1320 d, and 1325. Generally, the components of weight sensor 1300 are assembled so that weight sensor 1300 can detect a weight of items 315 positioned above or near weight sensor 1300.

Plates 1315 form surfaces that distribute the weight of items 315 across the surfaces. Plates 1315 may be made of any suitable material, such as, for example, metal and/or plastic. Items 315 may be positioned above or near plates 1315 and the weight of these items 315 may be distributed across plates 1315.

Load cells 1310 are positioned between plates 1315 a and 1315 b. Load cells 1310 produce electrical signals based on the weight experienced by the load cells 1310. For example, load cells 1310 may be transducers that convert an input mechanical force (e.g., weight, tension, compression, pressure, or torque) into an output electrical signal (e.g., current or voltage). As the input force increase, the output electrical signal may increase proportionally. Load cells 1310 may be any suitable type of load cell (e.g., hydraulic, pneumatic, and strain gauge). Although load cells 1310 are illustrated as being cylindrical in shape, they may be any suitable size and shape that is appropriate for the particular implementation contemplated.

The signals from load cells 1310 may be analyzed to determine an overall weight of items 315 positioned above or near weight sensor 1300. Load cells 1310 may be positioned such that the weight of items 315 positioned above or near weight sensor 1300 is evenly distributed to each load cell 1310. In the example of FIG. 13B, load cells 1310 are positioned substantially equidistant from corners of plates 1315 a and 1315 b. For example, load cell 1310 a is positioned a distance d1 from a corner of plates 1315 a and 1315 b. Load cell 1310 b is positioned a distance d2 from a corner of plates 1315 a and 1315 b. Load cell 1310 c is positioned a distance d3 from a corner of plates 1315 a and 1315 b. Load cell 1310 d is positioned a distance d4 from a corner of 1315 a and 1315 b. Distances d1, d2, d3 and d4 may be substantially equal to each other. This disclosure contemplates distances differing by 5 to 10 millimeters and still being considered substantially equal to each other. By positioning load cells 1310 substantially equal distances from corners of plates 1315 a and 1315 b, the weight of items positioned above or near weight sensor 1300 is evenly distributed across the load cells 1310. As a result, the total weight of items positioned above or near weight sensor 1300 can be determined by summing the weights experienced by the individual load cells 1310.

Load cells 1310 communicate electric signals that indicate a weight experienced by the load cells 1310. For example, the load cells 1310 may produce an electric current that varies depending on the weight or force experienced by the load cells 1310. Each load cell 1310 is coupled to a wire 1320 that carries the electric signal. In the example of FIG. 13B, load cell 1310 a is coupled to wire 1320 a; load cell 1310 b is coupled to wire 1320 b; load cell 1310 c is coupled to wire 1320 c; and load cell 1310 d is coupled to wire 1320 d. Wires 1320 are grouped together to form wire 1325 that extends away from weight sensor 1300. Wire 1325 carries the electric signals produced by load cells 1310 to a circuit board that communicates the signals to external system 485.

In certain embodiments, and as illustrated in FIG. 13C, multiple weight sensors 1300 may be coupled to a given physical shelf 305 of physical rack 210. For example, in the example illustrated in FIG. 13C, physical shelf 305 includes four weight sensors 1300. The locations of weight sensors 1300 in physical shelf 305 may define a set of zones of physical shelf 305. For example, first weight sensor 1300 a may define a first zone 325 a, second weight sensor 1300 b may define a second zone 325 b, third weight sensor 1300 c may define a third zone 325 c, and fourth weight sensor 1300 d may define a fourth zone 325 d. In certain embodiments, each zone 325 may be associated with a different physical item 315, such that each weight sensor 1300 is configured to detect weight changes associated with the removal of a specific item 315 from physical shelf 305. Virtual shelves 310 may similarly be divided in a set of zones 330 a through 330 d, with each virtual zone 330 associated with a given virtual item 320, to emulate zones 325 a through 325 d of physical shelves 305. In this manner, when a signal is received from a weight sensor 1300 a, indicating the removal of a physical item 315 a stored in first physical zone 325 a of physical shelf 305, the signal may be used to identify virtual item 320 a, stored in first virtual zone 330 a, based on the correspondence between first physical zone 325 a and first virtual zone 330 a.

b. Comparison Between Virtual Shopping Cart and Algorithmic Shopping Cart

In certain embodiments, virtual store tool 405 may be used in conjunction with an algorithm 488 trained to track customers 105 within physical store 100 and to determine the physical items 315 selected by a customer 105 during a shopping session in physical store 100, based on inputs 498 received from sensors 498 located in physical store 100. As described above, in the discussion of FIGS. 13A through 13D, sensors 498 may include cameras 1305 and weight sensors 1300.

Algorithm 488 may be programmed to determine the items 315 selected by customers 105 in physical store 100 in any suitable manner. For example, algorithm 488 may process video frames, received by external system 485 from the array of cameras 1305, to determine coordinates for customers 105 detected in the frames. Algorithm 488 may then timestamp these coordinates based on when the frames were received. Based on the coordinates and the timestamps, algorithm 488 may determine the positions of customers 105 in physical store 100. Algorithm 488 may also process signals received by external system 485 from weight sensors 1300, to determine when items 315 were removed from physical shelves 305. Using the positions of customers 105 in physical store 100, and the determinations of when items 315 were removed from physical shelves 305, algorithm 488 may determine which customers 105 took which items 315.

As an example of the use of virtual store tool 105 in conjunction with algorithm 488, virtual store tool 405 may be used to resolve discrepancies between the physical items 315 determined by algorithm 488 to have been selected by customer 105 during a shopping session in physical store 100 and the virtual items 320 determined by virtual store tool 405 to have been selected by customer 105 during the shopping session. When discrepancies exist between the physical items 315 determined by algorithm 488 to have been selected by customer 105 and the virtual items 320 determined by virtual store tool 405 to have been selected by customer 105, the determination made by virtual store tool 405 may also be used to improve the future accuracy of algorithm 488. FIGS. 14 through 16 are used to describe these aspects of virtual store tool 405.

FIG. 14 illustrates resolution component 475 of virtual store tool 405. Resolution component 475 is configured to compare virtual shopping cart 420 to algorithmic shopping cart 1420. This disclosure contemplates that virtual store tool 405 may receive algorithmic shopping cart 1420 from external system 485. Algorithmic shopping cart 1420 may include physical items 315 determined by an algorithm 488 to have been selected by customer 105, based on inputs received from sensors 498 (including cameras 1305 and weight sensors 1300) located in physical store 100. For example, algorithmic shopping cart may include first physical item 3151, second physical item 315 m, and third physical item 315 n. Each of physical items 3151 through 3135 n is associated with a given purchase price. For example, first physical item 3151 is associated with a first physical price 1440, second physical item 315 m is associated with a second physical price 1450, and third physical item 315 n is associated with a third physical price 1460. While illustrated in FIG. 14 as including three physical items 315, this disclosure contemplates that algorithmic shopping cart 1420 may include any number of physical items 315. Virtual shopping cart 420 includes first virtual item 320 i, second virtual item 320 j, and third virtual item 320 k, each determined by virtual store tool 405 to have been selected by customer 105 during a shopping session in physical store 100. Each of virtual items 320 i through 320 k is associated with a given purchase price. For example, first virtual item 320 i is associated with a first virtual price 1410, second virtual item 320 j is associated with a second virtual price 1420, and third virtual item 320 k is associated with a third virtual price 1425. While illustrated in FIG. 14 as including three virtual items 320, this disclosure contemplates that virtual shopping cart 420 may include any number of virtual items 320. Furthermore, virtual shopping cart 420 need not include the same number of items as algorithmic shopping cart 1420.

Resolution component 475 is configured to perform a comparison 1430 between the contents of virtual shopping cart 420 and the contents of algorithmic shopping cart 1420 to determine if any discrepancies 1435 exist between the two carts. Resolution component 475 may determine either that: (1) the two carts are consistent with one another; or (2) the two carts are inconsistent with one another. In certain embodiments, in response to determining that the two carts are inconsistent with one another, resolution component 475 may determine that (1) the two carts are inconsistent with one another because virtual cart 420 includes an error; or (2) the two carts are inconsistent with one another, because algorithmic cart 1420 includes an error.

Determining that the two carts are consistent with one another may include determining that first virtual item 320 i, present in virtual shopping cart 420, is configured to emulate first physical item 3151, which is present in algorithmic shopping cart 1420, second virtual item 320 j, present in virtual shopping cart 420, is configured to emulate second physical item 315 m, which is present in algorithmic shopping cart 1420, and third virtual item 320 k, present in virtual shopping cart 420, is configured to emulate third physical item 315 n, which is present in algorithmic shopping cart 1420. On the other hand, determining that the two carts are inconsistent with one another may include: (1) determining that virtual shopping cart 420 includes more virtual items 320 than algorithmic shopping cart 1420 includes physical items 315; (2) determining that virtual shopping cart 420 includes fewer virtual items 320 than algorithmic shopping cart 1420 includes physical items 315; (3) determining that a virtual item 320, present in virtual shopping cart 420, is configured to emulate a physical item 315, which is not present in algorithmic shopping cart 1420; or (4) determining that no virtual item 320, present in virtual shopping cart 420, is configured to emulate a physical item 315, present in algorithmic shopping cart 1420.

This disclosure contemplates that in embodiments in which resolution component 475 may determine that the two carts are inconsistent with one another because one of the two carts includes an error, resolution component 475 may determine that one of the two carts includes an error in any suitable manner. As an example, in certain embodiments, resolution component 475 may always determine that algorithmic shopping cart 1420 includes an error any time a discrepancy exists between virtual shopping cart 420 and algorithmic shopping cart 1420. As another example, resolution component 475 may determine that one of the carts includes an error, based on the type of discrepancy 1435 that exists between virtual cart 420 and algorithmic cart 1420. For example, resolution component 475 may be configured to determine that virtual shopping cart 420 includes an error when the discrepancy 1435 between the two carts is a result of differing quantities of a particular item between the two carts. For instance, virtual cart 420 may include one instance of first virtual item 320 i, configured to emulate first physical item 3151, while algorithmic shopping cart 1420 may include two instances of first physical item 3151. In such situations, it may be more likely that virtual shopping cart 420 includes an incorrect quantity of first virtual item 320 i, than algorithmic shopping cart 1420 includes an incorrect quantity of first physical item 3151, as it may be difficult to tell from camera feed segments 415 and/or 425 that customer 105 selected more than one physical item 3151 from a given physical shelf 305. On the other hand, the information received from weight sensors 1300 in physical store 100, may more accurately indicate that more than one physical item 3151 was selected from physical shelf 305. For discrepancies 1435 that do not include differing quantities of a particular item between the two carts, resolution component 475 may be configured to determine that algorithmic shopping cart 1420 includes an error, as a default.

As another example, resolution component 475 may be configured to determine that either virtual shopping cart 420 or algorithmic shopping cart 1420 includes an error based on input received from user 120. For example, in response to determining that a discrepancy 1435 exists between virtual shopping cart 420 and algorithmic shopping cart 1420, resolution component 475 may send a message to device 115, advising user 120 of the discrepancy 1435. User 120 may then send a response to virtual store tool 405 indicating either that virtual shopping cart 420 includes an error, or that algorithmic shopping cart 1420 includes an error. User 120 may determine that one of virtual shopping cart 420 and algorithmic shopping cart 1420 include an error in any suitable manner. As an example, user 120 may review camera feed segments 415 and/or 425 to either confirm the contents of virtual shopping cart 420 or determine that virtual shopping cart 420 includes an error. For example, if the discrepancy includes an absence of a physical item 315 from algorithmic shopping cart 1420, where the corresponding virtual item 320 is present in virtual shopping cart 420, user 120 may review camera feed segments 415 and/or 425 to confirm that the camera feed segments capture customer 105 selecting the physical item 315 from a physical rack 210. As another example, if the discrepancy includes the presence of a physical item 315 in algorithmic shopping cart 1420, where the corresponding virtual item 320 is absent from virtual shopping cart 420, user 120 may review camera feed segments 415 and/or 425 to either (1) confirm that user 120 never observes customer 105 selecting the physical item 315 from a physical rack 210; or (2) confirm that while user 120 views customer 105 selecting the physical item 315 from a physical rack 210 in camera feed segments 415 and/or 425, user 120 subsequently views the customer 105 setting down the physical item 315 and leaving the physical store 100 with the item 315.

Resolution component 475 is also configured to generate a receipt 1465 for customer 105. In certain embodiments, resolution component 475 generates receipt 1465 based on the contents of virtual shopping cart 420. For example, resolution component 475 may generate receipt 1465 based on the contents of virtual shopping cart 420 before performing comparison 1430. In other embodiments, resolution component 475 may generate receipt 1465 based on comparison 1430. For example, if resolution component 475 determines that virtual shopping cart 420 is consistent with algorithmic shopping cart 1420, resolution component 475 may generate receipt 1465 a for customer 105. Receipt 1465 a may include the total cost 1475 of first virtual item 320 i, second virtual item 320 j, and third virtual item 320 k, as determined from first virtual price 1410, second virtual price 1420, and third virtual price 1425. Here, since virtual cart 420 is consistent with algorithmic shopping cart 1420, determining the total cost 1475 of first virtual item 320 i, second virtual item 320 j, and third virtual item 320 k is equivalent to determining the total cost of first physical item 3151, second physical item 315 m, and third physical item 315 n. As another example, if resolution component 475 determines that virtual shopping cart 420 includes an error, resolution component 475 may generate receipt 1465 b for customer 105. Receipt 1465 b may include the total cost 1480 of first physical item 3151, second physical item 315 m, and third physical item 315 n, as determined from first physical price 1440, second physical price 1450, and third physical price 1460. As a further example, if resolution component 475 determines that algorithmic shopping cart 1420 includes an error, resolution component 475 may generate receipt 1465 c for customer 105. Receipt 1465 c may include the total cost 1475 of first virtual item 320 i, second virtual item 320 j, and third virtual item 320 k, as determined from first virtual price 1410, second virtual price 1420, and third virtual price 1425. Once resolution component 475 has generated a receipt 1465 for customer 105, resolution component 475 may charge customer 105, based on receipt 1465, and/or send receipt 1465 to device 125, belonging to customer 105.

Resolution component 475 may be a software module stored in memory 440 and executed by processor 435. An example of the operation of resolution component 475 is as follows: (1) receive virtual shopping cart 420 and algorithmic shopping cart 1420; (2) determine if the number of virtual items 320 in virtual shopping cart 420 is the same as the number of physical items 315 in algorithmic shopping cart 1420; (3) if the number of virtual items 320 in virtual shopping cart 420 is different from the number of physical items 315 in algorithmic shopping cart 1420, label the two carts as inconsistent; (4) if the number of virtual items 320 in virtual shopping cart 420 is the same as the number of physical items 315 in algorithmic shopping cart 1420, determine if virtual shopping cart 420 includes any virtual items 320 for which algorithmic shopping cart 1420 does not include a corresponding physical item 315; (5) if virtual shopping cart 420 includes any virtual items 320 for which algorithmic shopping cart 1420 does not include a corresponding physical item 315, label the two carts as inconsistent; (6) if virtual shopping cart 420 does not include any virtual items 320 for which algorithmic shopping 1420 does not include a corresponding physical item 315, label the two carts as consistent; (7) if the two carts are labelled as consistent generate receipt 1465 a; (8) if the two carts are labelled as inconsistent, determine whether virtual cart 420 includes an error; (9) if virtual cart 420 includes an error, generate receipt 1465 b; (10) if virtual cart 420 does not include an error, generate receipt 1465 c.

c. Algorithm Feedback

In certain embodiments, virtual store tool 405 may be used in conjunction with algorithm 488, to improve the accuracy of the determinations made by algorithm 488. Specifically, machine learning module 480 may provide feedback to algorithm 488, based on a comparison of the contents of virtual shopping cart 420 to the contents of algorithmic shopping cart 1420. FIG. 15 illustrates the operation of machine learning module 480.

As illustrated in FIG. 15, in certain embodiments, machine learning module 480 receives algorithmic shopping cart 1420 and virtual shopping cart 420. Machine learning module 480 may then perform a comparison 1430 of the contents of virtual shopping cart 420 and the contents of algorithmic shopping cart 1420, to determine if a discrepancy 1435 exists between the two carts. In certain other embodiments, machine learning module 480 may receive an indication that a discrepancy 1435 exists between virtual shopping cart 420 and algorithmic shopping cart 1420 directly from resolution component 475.

Discrepancy 1435 may include any inconsistency between virtual shopping cart 420 and algorithmic shopping cart 1420. For example, discrepancy 1435 may include an absence of a physical item 315 a from algorithmic shopping cart 1420, where the corresponding virtual item 320 a is present in virtual shopping cart 420. Such a discrepancy may occur when a weight sensor 1300 coupled to a physical shelf 305 on which physical item 315 a is placed, failed to detect the selection of the physical item 315 a from physical shelf 305. As another example, discrepancy 1435 may include the presence of a physical item 315 b in algorithmic shopping cart 1420, where the corresponding virtual item 320 b is absent from virtual shopping cart 420. Such a discrepancy may arise from algorithm 488 failing to detect that a customer 105, who initially selected physical item 315 b from a physical rack 210, put item 315 b down and did not leave physical store 100 with the item 315 b. As a further example, discrepancy 1435 may include an identification swap between a first customer 105 a and a second customer 105 b, such that a first item 315 a selected by first customer 105 a is absent from the algorithmic shopping cart 1420 assigned to first customer 105 a, and present in an algorithmic shopping cart 1420 assigned to second customer 105 b. Such an identification swap may occur in the customer tracking component of algorithm 488.

In response to determining that a discrepancy exists between algorithmic shopping cart 1420 and virtual shopping cart 420, machine learning module 480 may determine a subset 1500 of inputs received by sensors 498 (including cameras 1305 and weight sensors 1300) and associated with the discrepancy. As an example, machine learning module 480 may determine a timestamp range of camera feed segments 415 and/or 425 during which discrepancy 1435 occurred. For example, machine learning module 480 may determine that a virtual item 320 a was added to virtual shopping cart 420, based on a portion of customer 105's shopping session captured between a first timestamp and a second timestamp of camera feed segments 415 and/or 425, but that a corresponding physical item 315 a was not added to algorithmic shopping cart 1420. As a result, machine learning module 480 may determine a subset 1500 of inputs received from sensors 498 during the same time interval (i.e., the time interval occurring between the first timestamp and the second timestamp). Subset 1500 may include a subset 1505 of inputs received from cameras 1305 and/or a subset 1510 of inputs received from weight sensors 1300. In response to identifying subset 1500, associated with discrepancy 1435, machine learning module 480 may attach metadata 1540 to subset 1500. This disclosure contemplates that metadata 1540 may include any information explaining and/or addressing discrepancy 1435. For example, attaching metadata 1540 to subset 1500 may include attaching a label to one or more frames received from cameras 1305 indicating that the frames illustrate customer 105 a selecting item 315, rather than customer 105 b selecting the item, as originally determined by algorithm 488. In response to attaching metadata 1540 to subset 1500, external system 485 may use subset 1500 to retrain algorithm 488. In certain embodiments, retraining algorithm 488 may result in an improved accuracy of algorithm 488.

Machine learning module 480 may be a software module stored in memory 440 and executed by processor 435. An example of the operation of machine learning module 480 is as follows: (1) receive algorithmic shopping cart 1420; (2) receive virtual shopping cart 420; (3) compare the contents of virtual shopping cart 420 to the contents of algorithmic shopping cart 1420; (4) determine that discrepancy 1435 exists between virtual shopping cart 420 and algorithmic shopping cart 1420; (5) determine a subset 1500 of inputs received from sensors 498 (including cameras 1305 and weight sensors 1300); (6) attach metadata 1540 to subset 1500, so that external system 485 may use subset 1500 to retrain algorithm 488.

FIG. 16 presents a flowchart illustrating the manner by which virtual store tool 405 may use virtual shopping cart 420 to provide feedback to algorithm 488. In step 1605, resolution component 475 receives an algorithmic shopping cart 1420. Algorithmic shopping cart 1420 includes a set of physical items 315, determined by algorithm 488 to have been selected by a customer 105 during a shopping session in physical store 100, based on inputs received from sensors 498 located in physical store 100. In step 1610, resolution component 475 receives a virtual shopping cart 420. Virtual shopping cart 420 includes a set of virtual items 320. In certain embodiments, virtual items 320 were determined by a user 120 to have been selected by customer 105 during a shopping session in physical store 100, based on camera feed segments 415 and/or 425 capturing the shopping session of customer 105 in physical store 100.

In step 1615, resolution component 475 compares the contents of algorithmic shopping cart 1420 to the contents of virtual shopping cart 420. In step 1620, resolution component 475 determines whether a discrepancy 1435 exists between algorithmic shopping cart 1420 and virtual shopping cart 420. If, in step 1620, resolution component 475 determines that a discrepancy 1435 does not exist between algorithmic shopping cart 1420 and virtual shopping cart 420, then, in step 1640, resolution component 475 generates a receipt 1465 for the shopping session, based on the contents of virtual shopping cart 420, and sends receipt 1465 to a device 125 of customer 105. If, in step 1620, resolution component 475 determines that a discrepancy 1435 exists between algorithmic shopping cart 1420 and virtual shopping cart 420, then, in step 1625, machine learning module 480 determines a subset 1500 of the set of inputs received from sensors 498 associated with the discrepancy. In step 1630, machine learning module 480 attaches metadata 1540 to subset 1500. Metadata 1540 may explain discrepancy 1435. In step 1635, external system 485 may use subset 1500 to retrain algorithm 488. Additionally, in step 1640, resolution component 475 generates a receipt 1465 for the shopping session, based on the contents of virtual shopping cart 420, and sends receipt 1465 to a device 125 of customer 105. Modifications, additions, or omissions may be made to method 1600 depicted in FIG. 16. Method 1600 may include more, fewer, or other steps. For example, steps may be performed in parallel or in any suitable order. While discussed as virtual store tool 405 (or components thereof) performing the steps, any suitable component of system 400, such as device(s) 115 for example, may perform one or more steps of the method.

VII. Increasing the Efficiency of the Virtual Emulation Process

As described above, virtual store tool 405 may be used to virtually emulate a shopping session occurring in a physical store 100 and captured by camera feed segments 415 and/or 425. Using virtual store tool 405 in this manner may be a time-consuming process, especially if the tool is used to virtually emulate a large number of shopping sessions. Accordingly, virtual shopping tool 405 includes a number of features to increase the efficiency of the virtual emulation process. These may include: (1) providing suggestions of items that customer 105 may have selected during the shopping session, as determined by an external system 485; (2) enabling user 120 to efficiently navigate videos of the shopping session displayed on graphical user interface 700; and/or (3) providing an image of each customer 105 as he/she exited the physical store 100. Each of these features is described in further detail in the discussion that follows.

a. Algorithmic Suggestions

As described above, in certain embodiments, virtual store tool 405 may be used to independently verify determinations made by algorithm 488. For example, virtual store tool 405 may compare the contents of virtual shopping cart 420 to the contents of algorithmic cart 1420 to evaluate the accuracy of algorithmic cart 1420 and/or to provide feedback to algorithm 488, for use in training algorithm 488. In order to provide such an independent verification, in certain embodiments, virtual store tool 405 does not receive any information about the contents of algorithmic cart 1420 until after virtual shopping cart 420 has been generated. This may be desirable to prevent any of the determinations that have been made by algorithm 488 from biasing user 120. Preventing the determinations of algorithm 488 from biasing user 120 may be especially important early on in the training process for algorithm 488, when there exists a reasonable probability that the determinations made by algorithm 488 are incorrect, and/or when complex/unexpected scenarios arise within physical store 100, which may prove challenging for algorithm 488 to accurately process.

On the other hand, in situations in which there exists a high probability that the determinations made by algorithm 488 are correct (e.g., after algorithm 488 has undergone suitable training), the risk of virtual cart errors arising due to a biasing of user 120 towards erroneous determinations made by algorithm 488 may be minimal. At the same time, efficiency gains may occur when user 120 uses virtual store tool 405 to generate virtual cart 420 by confirming suggestions provided by algorithm 488 of the items selected by customer 105 during a shopping session in physical store 100 (or a portion of a shopping session in a physical store 100), rather than independently identifying the items selected by customer 105. For example, user 120 may be able to use virtual store tool 405 to generate virtual cart 420 more rapidly when aided by suggestions from algorithm 488.

i. Presenting, to a User of the Virtual Store Tool, a List of Products that have Been Identified by an Algorithm as Selected During a Shopping Session

FIGS. 17A and 17B present a first example of an embodiment in which virtual store tool 102 presents suggestions received from algorithm 488 to user 120. Specifically, FIGS. 17A and 17B present an example of an embodiment in which virtual store tool 102 presents a list 1704 of products that have been determined by algorithm 488 to have been selected by a customer 105 a during a shopping session in physical store 100 to a user 120 who is using virtual store tool 405 to virtually emulate the shopping session. User 120 may use the products in list 1704 as suggestions/hints of the products user 120 should expect to view customer 105 a selecting during his/her shopping session. FIG. 17A presents an example of the components of system 400 that may be involved in the process of virtual store tool 405 displaying product list 1704 on the graphical user interface 700 that is presented to user 120 on device 115, and FIG. 17B presents an example embodiment of graphical user interface 700 that includes product list 1704.

As illustrated in FIG. 17A, in certain embodiments, virtual store tool 405 receives an algorithmic shopping cart 1420 that includes a set of physical items 315 determined by an algorithm 488 of external system 485 to have been selected by a customer 105 a during a shopping session in physical store 100. Algorithm 488 may have determined that customer 105 a selected the set of physical items 315 based on inputs received from sensors 498 located in physical store 100. As described above, in the discussion of FIGS. 13A through 13D, sensors 498 may include cameras 1305 and weight sensors 1300 located in physical store 100. In response to receiving algorithmic shopping cart 1420, in certain embodiments, for each physical item 315 j/q included in algorithmic shopping cart 1420, virtual store tool 405 displays a corresponding virtual item 320 j/q in a product list 1704 on graphical user interface 700. For example, as illustrated in FIGS. 17A and 17B, product list 1704 includes virtual item 320 j and virtual item 320 q, indicating that algorithm 488 determined that customer 105 a selected physical item 315 j and physical item 315 q during a shopping session in physical store 100. In some embodiments, algorithmic shopping cart 1420 may be a partial shopping cart corresponding to a portion of customer 105 a's shopping session in physical store 100. For example, algorithmic shopping cart 1420 may correspond to a portion of customer 105 a's shopping session for which the inputs received from sensors 498 indicate that verification of the algorithmic determination is desirable (e.g., the inputs indicate that there exists a probability that one or more items in algorithmic shopping cart 1420 is incorrect that is greater than a threshold).

A. Information Included in the Product List

Virtual store tool 405 may include any information received from algorithm 488 (e.g., algorithmic shopping cart 1420) in product list 1704, and may display this information in any suitable manner. As an example, while FIG. 17B depicts product list 1704 as a region of graphical user interface 700 in which virtual items 320 j/q are displayed as graphical representations of the corresponding physical items 315 j/q, virtual store tool 405 may present an identification of items 315 j/q to user 120 in any suitable manner. For example, in certain embodiments, product list 1704 includes textual descriptions of physical items 315 j/q determined by algorithm 488 to have been selected by customer 105 a.

As another example, product list 1704 may include additional information (other than an identification of physical items 315 j/q determined by algorithm 488 to have been selected by customer 105 a) that may be useful to user 120 in populating virtual shopping cart 420. For example, in certain embodiments, product list 1704 may indicate a quantity of each item 320 j/q that algorithm 488 determined was selected by customer 105 a during his/her shopping session. As an example, product list 1704 may include a number to the left or right of each virtual item 320 j/q displayed within the list, indicating the quantity of the corresponding physical item 315 j/q that algorithm 488 determined was selected by customer 105 a. In some embodiments, product list 1704 does not include any indication of the quantity of each physical item 315 that algorithm 488 determined was selected by customer 105 a. For example, in such embodiments, a product list 1704, as depicted in FIG. 17B, may correspond to a situation in which algorithm 488 has determined that customer 105 a selected a single physical item 315 j and a single physical item 315 q (e.g., virtual store tool 405 received an algorithmic shopping cart 1420 from external system 485 that included a single physical item 315 j and a single physical item 315 q), as well as a situation in which algorithm 488 has determined that customer 105 a selected 10 physical items 315 j and 2 physical items 315 q (e.g., virtual store tool 405 received an algorithmic shopping cart 1420 from external system 485 that include 10 physical items 315 j and 2 physical items 315 q).

B. Timing of Displaying Items in the Product List

Virtual store tool 405 may add virtual products 320 j/q to product list 1704 at any suitable point in time while user 120 is using graphical user interface 700 to virtually emulate a shopping session of a customer 105 a. For example, virtual store tool 405 may display a virtual item 320 j/q in product list 1704 for each physical item 315 j/q that is included in the algorithmic shopping cart 1420 that was received from external system 485, at the start of the virtual emulation session. For instance, when graphical user interface 700 first displays product list 1704 on display 410, product list 1704 may already include virtual items 320 j/q. This may occur, for example, in embodiments in which customer 105 a's shopping session (or the portion of customer 105 a's shopping session subject to virtual emulation) has already ended prior to the use of virtual store tool 405 to virtually emulate the shopping session (or the portion of the shopping session subject to virtual emulation). virtual store tool Specifically, in certain embodiments, the length of camera feed segments 415 may correspond to and/or exceed the length of customer 105 a's shopping session (or the length of the portion of the shopping session subject to virtual emulation), such that when virtual store tool 405 displays camera feed segments 415 on graphical interface 700, the shopping session (or portion of the shopping session) has already ended. In such embodiments, algorithm 488 may have already used the information collected from sensors 498 during the shopping session (or the portion of the shopping session subject to virtual emulation) to populate algorithmic shopping cart 1420, and subsequently provided the cart to virtual store tool 405 prior to virtual store tool 405 displaying graphical user interface 700. Accordingly, virtual store tool 405 may display a virtual item 320 j/q in product list 1704 for each physical item 315 j/q included in algorithmic cart 1420, when virtual store tool 405 first displays graphical user interface 700 at the beginning of the virtual emulation session.

Product list 1704 may also include one or more virtual items 320 j when first displayed on graphical user interface 700 in embodiments in which virtual store tool 405 is being used to virtually emulate a shopping session (or a portion of a shopping session) that is in progress. Specifically, in certain embodiments, the length of camera feed segments 415 is less than the length of customer 105 a's shopping session (of the portion of the shopping session subject to virtual emulation), such that when virtual store tool 405 initially displays camera feed segments 415 on graphical interface 700, the shopping session is still ongoing. In such embodiments, algorithm 488 may have already populated algorithmic shopping cart 1420 with one or more physical items 315 j that it determined customer 105 a selected during a first portion of the shopping session captured by camera feed segments 415. Algorithm 488 may provide this partial algorithmic shopping cart 1420 to virtual store tool 405, enabling the tool to display a virtual item 320 j in product list 1704 for each physical item 315 j included in partial algorithmic cart 1420. In such embodiments, virtual store tool 405 may update product list 1704 in response to algorithm 488 adding additional physical items 315 q to algorithmic shopping cart 1420. For example, in certain embodiments, virtual store tool 405 may update product list 1704 in conjunction with displaying a new set of camera feed segments 415, which correspond to a time interval immediately following the time interval associated with the previous set of camera feed segments 415. The updated product list 1704 may include virtual items 320 q corresponding to physical items 315 q determined by algorithm 488 to have been selected by customer 105 during the time interval covered by the new camera feed segments 415. In some embodiments, virtual store tool 405 may update product list 1704 in response to receiving an updated algorithmic shopping cart 1420 that includes a new physical item 315 q.

As another example of the timing at which virtual store tool 405 may display virtual items 320 j/q in product list 1704, in certain embodiments, when virtual store tool 405 first displays graphical user interface 700, product list 1704 is empty. In such embodiments, virtual store tool 405 may add virtual items 320 j/q to product list 1704 as the virtual emulation session progresses (e.g., as the playback of camera feed segments 415, which captured the shopping session, progresses). As a specific example, in certain embodiments, virtual store tool 405 adds a virtual item 320 j (corresponding to a physical item 315 j) to product list 1704 when the playback progress of camera feed segments 415 and/or 425 reaches a specified time interval before the playback time at which camera feed segments 415 and/or 425 should have captured customer 105 a selecting physical item 315 j, as determined by algorithm 488. For example, consider a scenario in which the specified time interval is 15 seconds, and algorithm 488 has determined that customer 105 a selected physical item 315 j at 3:15:10 pm. In such embodiments, virtual cart tool 405 will then add virtual item 320 j to product list 1704 when the playback progress of camera feed segments 415 and/or 425 reaches a playback time corresponding to 3:14:55 pm.

C. Use of the Product List

The virtual items 320 j/q displayed in product list 1704 may aid user 120 in efficiently emulating a shopping session of a customer 105 a in physical store 100. In particular, because the virtual items 320 j/q displayed in product list 1704 correspond to physical items 315 j/q determined by algorithm 488 to have been selected by customer 105 a during a shopping session in physical store 100, the virtual items 320 j/q displayed in product list 1704 may provide suggestions to user 120 of items 320 j/q to consider adding to virtual shopping cart 420. User 120 may use the suggestions provided by product list 1704 in any suitable manner.

As a first example, in certain embodiments, user 120 may select for consideration one of the virtual items 320 j/q displayed in product list 1704 and determine whether customer 105 a selected the corresponding physical item 315 j/q during his/her shopping session in physical store 100. User 120 may select for consideration a virtual item 320 j/q from product list 1704 in any suitable manner. For example, user 120 may select virtual item 320 j for consideration by clicking on the graphical icon corresponding to the virtual item, double-clicking on the graphical icon, and/or interacting with the graphical icon in any other suitable manner.

Graphical user interface 700 may include any of a number of features that may help user 120 in directing his/her focus towards a given virtual item 320 j/q that is displayed in product list 1704 and that has been selected for consideration by user 120. As an example, in response to selecting a first virtual item 320 j displayed in product list 1704, virtual store tool 405 may highlight, in virtual layout 205, the virtual rack 230 m on which virtual item 320 j is stored. Highlighting virtual rack 230 m may include any method of distinguishing virtual rack 230 m from the other virtual racks 230. For example, highlighting virtual rack 230 m may include placing a frame around virtual rack 230 m, applying a color/shading to virtual rack 230 m (as illustrated in FIG. 17B), and/or any other suitable method of distinguishing virtual rack 230 m from the remaining virtual racks 230. As another example, in certain embodiments, in response to selecting a first virtual item 320 j displayed in product list 1704, virtual store tool 405 may display, in region 905 of graphical user interface 700, the virtual rack 230 m on which virtual item 320 j is stored. In some such embodiments, virtual store tool 405 may also highlight virtual item 320 j on virtual rack 230 m. Highlighting virtual item 320 a may include any method of distinguishing virtual item 320 j from the remaining virtual items 320 on virtual rack 230 m. For example, highlighting virtual item 320 j may include placing a frame around virtual item 320 j, enlarging virtual item 320 j compared to the other virtual items 320, changing a color of the background around virtual item 320 j, and/or any other suitable method of distinguishing virtual item 320 j from the remaining virtual items 320. As a further example, in certain embodiments, in response to selecting virtual item 320 j displayed in product list 1704, virtual store tool 405 may display camera feed segment 1702, which includes video of the physical rack 210 m in physical store 100 to which virtual rack 230 m is assigned. For example, as illustrated in FIG. 17A, camera feed segment 1702 may have been captured by a rack camera 495 directed at the physical rack 210 m to which virtual rack 230 m is assigned. As illustrated in FIG. 17B, in certain embodiments, virtual store tool 405 displays camera feed segment 1702 on graphical user interface 700 in a separate region of display 410 from camera feed segments 415 a through 415 f, and/or enlarged as compared to camera feed segments 415 a through 415 f. This may aid user 120 in directing his/her attention towards camera feed segment 1702.

As a second example of the manner by which user 120 may use the suggestions provided by product list 1704, in certain embodiments, user 120 may add any of the virtual items 320 j/q displayed in product list 1704 to virtual shopping cart 420, while virtually emulating a shopping session of a customer 105 a in physical store 100. For example, user 120 may consider a first virtual item 320 j displayed in product list 1704. As described above, by displaying first virtual item 320 j in product list 1704, virtual store tool 405 may indicate to user 120 that an external system 485 has determined that customer 105 a selected the corresponding physical item 315 j during the shopping session. User 120 may then confirm that customer 105 a selected the corresponding physical item 315 j by observing customer 105 a selecting physical item 315 j on camera feed segment 1702 and/or any of camera feed segments 415 a through 415 f, displayed on graphical user interface 700. In response to confirming that customer 105 a selected physical item 315 j, user 120 may add virtual item 320 j to virtual shopping cart 420, thereby virtually emulating customer 105 a's selecting of physical item 315 j. In certain embodiments, user 120 may add virtual item 320 j to virtual shopping cart 420 by selecting virtual item 320 j directly from product list 1704. For example, user 120 may (1) drag virtual item 320 j from product list 1704 to virtual shopping cart 420 and drop virtual item 320 j in virtual shopping cart 420, (2) double-click on virtual item 320 j in product list 1704 to add virtual item 320 j to virtual shopping cart 420, and/or (3) interact with graphical user interface 700 in any other suitable way to indicate that virtual item 320 j should be moved from product list 1704 to virtual shopping cart 420. In certain embodiments, user 120 may also or alternatively add virtual item 320 j to virtual shopping cart 420 by selecting virtual item 320 j from virtual rack 230 m, as described in Part IV above. In certain embodiments, in response to user 120 placing virtual item 320 j in virtual shopping cart 420, virtual store tool 405 removes virtual item 320 j from product list 1704.

As a third example of the manner by which user 120 may use the suggestions provided by product list 1704, in certain embodiments, user 120 may use product list 1704 to identify errors in algorithmic shopping cart 1420. For example, user 120 may consider a second virtual item 320 q, corresponding to physical item 315 q, presented in product list 1704. User 120 may then observe camera feed segment 1702 and/or any of camera feed segments 415 a through 415 f that include video of physical rack 210 m, on which physical item 315 q is stored, to determine that customer 105 a did not select physical item 315 q during his/her shopping session in physical store 100. Accordingly, because the virtual items 320 j/q displayed in product list 1704 correspond to physical items 315 j/q that algorithm 488 determined were selected by customer 105 a during the shopping session, by determining that customer 105 a did not select physical item 315 q, user 120 determined that the inclusion of physical item 315 q in algorithmic shopping cart 1420 is an error. In certain embodiments, in response to determining that customer 105 a did not select physical item 315 q, user 120 may instruct virtual store tool 405 to remove virtual item 320 q from product list 1704. In some embodiments, the removal of virtual item 320 q from product list 1704 and/or the lack of inclusion of virtual item 320 q in virtual shopping cart 420 may be used to provide feedback to algorithm 488, in a similar manner as described above, in the discussion of FIGS. 15 and 16.

ii. Prepopulating a Virtual Shopping Cart with Products that have Been Identified by an Algorithm as Selected During a Shopping Session, Prior to Virtual Emulation of that Shopping Session

In certain embodiments, virtual store tool 102 may present suggestions received from algorithm 488 to user 120 by placing these suggestions directly in virtual shopping cart 420. For example, when graphical user interface 700 first displays virtual shopping cart 420 on display 410, virtual shopping cart 420 may already include virtual items 320 j/q, where each virtual item 320 j/q corresponds to a physical item 315 j/q determined by algorithm 488 to have been selected by customer 105 a during his/her shopping session in physical store 100. User 120 may then view camera feed segments 1702 and/or 415 a through 415 f to determine whether the prepopulated virtual shopping cart 420 accurately reflects the physical items 315 j selected by customer 105 a during his/her shopping session. If user 120 determines that the prepopulated virtual shopping cart 420 does not accurately reflect the physical items 315 j selected by customer 105 a during his/her shopping session, user 120 may modify virtual shopping cart 420 by: (1) adjusting a quantity of a virtual item 320 j in virtual shopping cart 420; (2) removing a virtual item 320 q from virtual shopping cart 420; and/or (3) adding a virtual item 320 to virtual shopping cart 420.

In a similar manner as described above for embodiments in which the algorithmic suggestions are displayed in product list 1704, in certain embodiments in which virtual shopping cart 420 is prepopulated with algorithmic suggestions of virtual items 320 j/q, user 120 may interact with the graphical icons assigned to the virtual items 320 j/q that are displayed in the prepopulated virtual shopping cart 420. In particular, user 120 may interact with a graphical icon assigned to a virtual item 320 j/q to cause virtual store tool 405 to display information that may aid user 120 in determining whether customer 105 a actually selected the corresponding physical item 315 j/q during his/her shopping session in physical store 100, thereby evaluating the algorithmic suggestion of virtual item 302 j/q. For example, in certain embodiments, in response to selecting a first virtual item 320 j displayed in virtual shopping cart 420 (e.g., clicking on the graphical icon corresponding to the virtual item, double-clicking on the graphical icon, and/or interacting with the graphical icon in any other suitable manner), virtual store tool 405 may: (1) highlight, in virtual layout 205, the virtual rack 230 m on which virtual item 320 j is stored; (2) display, in region 905 of graphical user interface 700, the virtual rack 230 m on which virtual item 320 j is stored; (3) highlight, on virtual rack 230 m, virtual item 320 j; (4) display camera feed segment 1702, which includes video of the physical rack 210 m of physical store 100 to which virtual rack 230 m is assigned; and/or (5) display any other suitable information that may aid user 120 in evaluating the accuracy of the contents of prepopulated virtual shopping cart 420. As described above, highlighting virtual rack 230 m in virtual layout 205 may include placing a frame around virtual rack 230 m, applying a color/shading to virtual rack 230 m, and/or any other suitable method of distinguishing virtual rack 230 m from the remaining virtual racks 230. Similarly, as described above, highlighting virtual item 320 j on virtual rack 230 m may include placing a frame around virtual item 320 j, enlarging virtual item 320 j compared to the other virtual items 320, changing a color of the background around virtual item 320 j, and/or any other suitable method of distinguishing virtual item 320 j from the remaining virtual items 320.

iii. Using Video Markers and Highlighting to Present Algorithmic Suggestions to a User

In addition to/instead of presenting algorithmic suggestions to user 120 as product list 1704 and/or in virtual shopping cart 420, virtual store tool 405 may present algorithmic suggestions to user 120 in several different manners, some of which were presented in Section IV above. For example, as discussed in Section IV, in certain embodiments, virtual store tool 102 presents suggestions received from algorithm 488 to user 120 by displaying markers 715 a/b on slider bar 705, where each marker 715 a/b indicates that an event occurred within physical store 100 at the marker playback time. Such events may include customer 105 a approaching a physical rack 210 a and/or selecting an item 305 from physical rack 210 a, where the occurrence of the event was detected by algorithm 488, based on inputs received from sensors 498 located in physical store 100. As another example, as discussed in Section IV above, in certain embodiments, virtual store tool 102 presents suggestions received from algorithm 488 to user 120 by highlighting a virtual rack 230 in virtual layout 205, and/or highlighting a virtual item 320 on virtual rack 230. For example, virtual store tool 102 may highlight virtual rack 230 a in response to receiving an indication from algorithm 488 that an event associated with physical rack 210 a occurred (e.g., customer 105 a approaching physical rack 210 a and/or selecting an item 305 from physical rack 210 a). Similarly, virtual store tool 102 may highlight virtual item 320 f on virtual rack 230 a in response to receiving an indication from algorithm 488 that customer 105 a selected physical item 315 f from physical rack 210 a.

Virtual store tool 405 may also apply highlighting to virtual items 320 j/q that are displayed on virtual rack 230 m to distinguish between those items that are displayed on the rack and are also stored in algorithmic shopping cart 1420 and those items that are displayed on the rack and are also stored in virtual shopping cart 420. As an example, in certain embodiments, virtual store tool 405 may display a first type of highlighting around each virtual item 320 j/q displayed on virtual rack 230 m (displayed in region 905 of graphical user interface 700) that corresponds to a physical item 315 j/q that algorithm 488 determined customer 105 selected during a shopping session in physical store 100. Virtual store tool 405 may also display a second type of highlighting around each virtual item 320 j (displayed on virtual rack 230 m in region 905 of graphical user interface 700) that is also present in virtual shopping cart 410. Displaying the first and second types of highlighting around virtual items 320 j/q may include placing frames around virtual items 320 j/q, changing the color of the background surrounding virtual items 320 j/q, and/or any other manner of distinguishing virtual items 320 j/q from the remaining virtual items displayed on virtual rack 230 m in region 905 of graphical user interface 700. In certain embodiments, the first type of highlighting is different from the second type of highlighting. For example, the first type of highlighting may correspond to a first color frame and/or background around a virtual item 320 j, and the second type of highlighting may correspond to a second color frame and/or background around a virtual item 320 q. As a specific example of the use of these two types of highlighting, virtual store tool 405 may initially change the background around virtual items 320 j and 320 q (displayed on virtual rack 230 in region 905) to a first color (e.g., red), in response to receiving information indicating that the corresponding physical items 315 j and 315 q were added by algorithm 488 to algorithmic shopping cart 1420. Then, in response to user 120 adding virtual item 320 j to virtual shopping cart 420, virtual store tool 405 may change the background around virtual item 320 j from the first color to a second color (e.g., green). In this manner, when user 120 views virtual rack 230 m (as displayed in region 905 of graphical user interface 700) he/she may easily see, based on the presence of the second type of highlighting around virtual item 320 j, that he/she has already considered virtual item 320 j and determined that customer 105 a selected the corresponding physical item 315 j (e.g., user 120 has already added virtual item 320 j to virtual shopping cart 420). Similarly, based on the presence of the first type of highlighting around virtual item 320 q, user 120 may easily determine that he/she has not yet considered the algorithmic suggestion of virtual item 320 q (and/or that he/she may have already considered the algorithmic suggestion but determined that customer 105 a did not select the corresponding physical item 315 q). Accordingly, user 120 may direct his/her attention towards virtual item 320 q in order to evaluate this algorithmic suggestion (and/or to confirm a previous determination that the algorithmic suggestion was incorrect).

Placing highlighting around a virtual item 320 j may also include adding any suitable information to the highlighting. For example, placing the first type of highlighting around a virtual item 320 j may include placing an indication of the quantity of the corresponding physical item 315 j that is present in algorithmic shopping cart 1420. Similarly, placing the second type of highlighting around a virtual item 320 q may include placing an indication of the quantity of the virtual item in virtual shopping cart 420. As a specific example of the use of these two types of highlighting, virtual store tool 405 may initially place frames of a first color (e.g., red) around virtual items 320 j and 320 q (displayed on virtual rack 230 in region 905), in response to receiving information indicating that the corresponding physical items 315 j and 315 q were added by algorithm 488 to algorithmic shopping cart 1420. Virtual store tool 405 may also indicate within each frame a quantity of the corresponding physical item 315 j/q that is present in algorithmic shopping cart 1420. Then, in response to user 120 adding a different quantity of virtual item 320 j to virtual shopping cart 420, virtual store tool 405 may change the color of the frame around virtual item 320 j from the first color to a second color (e.g., green), and may update the quantity indicated within the frame to correspond to the quantity of virtual item 320 j included in virtual shopping cart 420. The use of highlighting in this manner may help user 120 to easily identify virtual items 320 j to which the user may wish to direct further attention. For example, prior to issuing a receipt to customer 105 a, based on virtual shopping cart 420, user 120 may wish to double-check one or more of the decisions that he/she made while populating virtual shopping cart 420 that conflicted with determinations made by algorithm 488. Accordingly, user 120 may use the second type of highlighting to easily identify those virtual items 320 j for which the user's decisions conflicted with the algorithm's determinations.

Virtual store tool 405 may additionally apply highlighting to physical items 315 j/q that are depicted in camera feed segments 415/1702. As an example, in certain embodiments, virtual store stool 405 may place a bounding box around each physical item 315 j/q depicted in camera feed segments 415/1702. This bounding box may correspond to a bounding box that was used by algorithm 488 to track items selected by customer 105 a in physical store 100. In certain embodiments, the bounding box around a physical item 315 j/q is displayed in camera feed segments 415/1702 using the same color as the highlighting applied to the corresponding virtual item 320 j/q.

b. Video Display and Control

While providing algorithmic suggestions to user 120 is one way in which virtual store tool 405 may aid user 120 in efficiently emulating a shopping session of customer 105, graphical user interface 700 may include a number of additional features to help increase efficiency. As an example, in certain embodiments, virtual store tool 405 enables a user 120 to interact with camera feed segments 415/1702 to zoom in on an area of interest and/or pan over to an area of interest. As another example, in certain embodiments, virtual store tool 405 displays at least one camera feed segment 1702 in a somewhat central location on graphical user interface 700, separate from the remaining camera feed segments 415 a through 415 f. In some such embodiments, camera feed segment 1702 is enlarged as compared with the remaining camera feed segments 415 a through 415 f. For example, as illustrated in FIG. 17, virtual store tool 405 may place enlarged camera feed segment 1702 near the upper center of graphical user interface 700. Camera feed segment 1702 may correspond to the camera feed segment currently of interest to user 120. Displaying camera feed segment 1702 separate from and/or enlarged as compared to camera feed segments 415 may aid user 120 in maintaining his/her focus on the camera feed segment.

Enlarged camera feed segment 1702 may correspond to any of the camera feeds received by virtual store tool 405 from layout cameras 490 and/or rack cameras 495. As an example, in certain embodiments, and as illustrated in FIG. 17B, camera feed segment 1702 may correspond to one of the camera feed segments 415 a through 415 f (e.g., camera feed segment 415 c), which may also be displayed on graphical user interface 700, for example, to the right of camera feed segment 1702. In certain embodiments, user 120 may select one of camera feed segments 415 a through 415 f for display as camera feed segment 1702. For example, virtual store tool 405 may display camera feed segment 415 c as camera feed segment 1702, in response to user 120 selecting camera feed segment 415 c (e.g., clicking on camera feed segment 415 c, double-clicking on camera feed segment 415 c, and/or any other suitable manner of selecting camera feed segment 415 c from the set of camera feed segments 415 a through 415 f, displayed on graphical user interface 700).

As another example, in certain embodiments, camera feed segment 1702 may correspond to a camera feed segment received from a rack camera 495 located in physical store 100. For example, in certain embodiments, virtual store tool 405 may display a camera feed segment received from a rack camera 495 directed at a physical rack 210 m on which physical item 315 j is stored as camera feed segment 1702, in response to user 120 selecting a virtual item 320 j (configured to emulate physical item 315 j) from product list 1704. In some embodiments, virtual store tool 405 may display a camera feed segment received from a rack camera 495 directed at a physical rack 210 m as camera feed segment 1702, in response to user 120 selecting virtual rack 230 m (configured to emulate physical rack 210 m) from virtual layout 205. In certain embodiments multiple rack cameras 495 may be assigned to a given physical rack 210 m. In such embodiments, virtual store tool 405 may display icons 1706 on virtual layout 205, where the locations of icons 1706 on virtual layout 205 emulate the physical locations of rack cameras 495 within physical layout 200. In response to a user 120 selecting an icon 1706 a/b (e.g., clicking on the icon, double-clicking on the icon, and/or any other suitable method of selecting the icon), virtual store tool 405 may display, as camera feed segment 1702, a camera feed segment that was captured by the rack camera 495 corresponding to the icon 1706 a/b.

c. Images from Entrance and Exit Cameras

As another example of a way in which virtual store tool 405 may aid user 120 in efficiently emulating a shopping session of customer 105 a in physical store 100, in certain embodiments, virtual store tool 405 displays an image 1710 of customer 105 a, captured when the customer exited physical store 100. For example, in certain embodiments in which physical store 100 includes turnstiles 510 to control the entry/exit of persons 105 into/out of the store (as depicted in FIG. 5A), physical store 100 may include a camera configured to take an image 1710 of customer 105 a as customer 105 a exits through a turnstile 510. Displaying image 1710 may help user 120 to easily confirm the accuracy of virtual shopping cart 420. For example, user 120 may use image 1710 to confirm that virtual shopping cart 420 is accurate, where virtual shopping cart 420 includes a single virtual item 320 j and image 1710 depicts customer 105 a exiting physical store 100 while carrying the corresponding physical item 315 j. Similarly, user 120 may use image 1710 to determine that virtual shopping cart 420 is inaccurate, where virtual shopping cart 410 includes a single virtual item 320 j and image 1710 depicts customer 105 a exiting physical store 100 without the corresponding physical item 315 j and/or with a different physical item 315 a.

In addition to or instead of displaying image 1710 of customer 105 a, in certain embodiments, and as described above in the discussion of FIG. 7B, virtual store tool 405 displays an image 725 of customer 105 a, captured when the customer entered physical store 100. Displaying image 725 may help user 120 to easily identify the customer 105 a whose shopping session virtual store tool 405 is being used to virtually emulate. This may be desirable, for example, in embodiments in which multiple customers are depicted in video segments 415 a through 415 f and/or 1702. As a specific example, in certain embodiments in which virtual store tool 102 presents a list 1704 of products that have been determined by algorithm 488 to have been selected by a customer 105 a during a shopping session in physical store 100, algorithm 488 may have mistakenly tracked the wrong customer (i.e., a customer other than customer 105 a). In such embodiments, video segments 415 a through 415 f and/or 1702 may indeed depict a customer selecting one or more of the items displayed in product list 1704; however, this customer is not the customer 105 a to which algorithm 488 assigned the items. Accordingly, by enabling user 120 to identify the customer 105 a for whom the virtual emulation is being performed, image 725 and/or 1710 may help user 120 to determine that customer 105 a did not select the one or more items displayed in product list 1704 that were selected by the other customer.

d. Method for Virtually Emulating a Physical Shopping Session Using Algorithmic Suggestions

FIG. 18 presents a flowchart (described in conjunction with elements of FIGS. 17A and 17B) illustrating an example method 1800 by which virtual store tool 405 may present algorithmic suggestions to user 120, and user 120 may use these suggestions while virtually emulating a shopping session of a customer 105 a in physical store 100. Specifically, method 1800 illustrates the use of algorithmic suggestions in the form of product list 1704, where each virtual item 320 displayed in product list 1704 corresponds to a physical item 315 that algorithm 488 determined that customer 105 a selected during the shopping session.

In step 1802 virtual store tool 405 receives algorithmic shopping cart 1420. For example, virtual store tool 405 may receive algorithmic shopping cart 1420 from external system 485. Algorithmic shopping cart 1420 may include a set of physical items 315 determined by algorithm 488 to have been selected by customer 105 a, based on inputs received from sensors 498 located in physical store 100. In step 1804 virtual store tool 405 uses algorithmic shopping cart 1420 to display product list 1704. For example, for each physical item 315 present in algorithmic shopping cart 1420, virtual store tool 405 displays a corresponding virtual item 320 in product list 1704. In certain embodiments, product list 1704 includes an indication of the quantity of each physical item 315, correspond to a virtual item 320 displayed in product list 1704, that algorithm 488 determined was selected by customer 105. For example, in certain embodiments, product list 1704 may include a number to the left or right of each virtual item 320 displayed within the list, indicating the quantity of the corresponding physical item 315 that algorithm 488 determined was selected by customer 105 a.

In step 1806 virtual store tool 405 considers a first virtual item 320 from product list 1704. For example, in certain embodiments, virtual store tool 405 receives an indication that user 120 has selected the virtual item 320 from product list 1704. User 120 may have selected virtual item 320 from product list 1704 in any suitable manner. For example, in certain embodiments, selecting virtual item 320 from product list 1704 may include clicking on a graphical icon displayed in product list 1704 that corresponds to virtual item 320, double-clicking on the graphical icon, and/or interacting with the graphical icon in any other suitable manner. In step 1808 virtual store tool 405 displays video segment 1702. Video segment 1702 includes video of the physical rack 210 in physical store 100 on which the physical item 315 corresponding to the selected virtual item 320 resides. In certain embodiments, video segment 1702 is a camera feed segment captured by a rack camera 495 that was directed at the physical rack 210 on which the physical item 315 corresponding to the selected virtual item 320 resides.

In step 1810 virtual store tool 405 determines whether the tool has received information identifying the virtual item 320 selected from product list 1704. This information may include any information that may indicate to virtual store tool 405 that the tool should store the selected virtual item 320 in virtual shopping cart 420. As an example, in certain embodiments, receiving information identifying the selected virtual item 320 includes receiving information associated with dragging and dropping virtual item 320 from product list 1704 to virtual shopping cart 420. As another example, in certain embodiments, receiving information identifying the selected virtual item 320 includes receiving information associated with dragging and dropping virtual item 320 from virtual rack 230, displayed in region 905 of display 410, to virtual shopping cart 420.

If, in step 1810 virtual store tool 405 determines that the tool has received information identifying the selected virtual item 320, in step 1820 virtual store tool 405 adds virtual item 320 to virtual shopping cart 420. Method 1800 then proceeds to step 1822. If, in step 1810 virtual store tool 405 determines that the tool has not received information identifying the selected virtual item 320, in step 1812, in certain embodiments, virtual store tool 405 removes virtual item 320 from product list 1704. As an example, in certain embodiments, virtual store tool 405 receives an indication that customer 105 a did not select the physical item 315 corresponding to virtual item 320 during the shopping session. For instance, virtual store tool 405 may receive an indication from user 120 that user 120 did not view customer 105 a selecting the physical item 315 corresponding to virtual item 320 in camera segments 415/1702. In response to receiving the indication that customer 105 a did not select the physical item 315 corresponding to virtual item 320 during the shopping session, virtual store tool 405 removes virtual item 320 from product list 1704. As another example, in certain embodiments, virtual store tool 405 removes virtual item 320 from product list 1704 if virtual store tool 405 does not receive information identifying virtual item 320 before: (1) playback of video segment 1702 completes, (2) virtual store tool 405 considers a next virtual item 320 from product list 1704, and/or (3) virtual store tool 405 issues a receipt for the shopping session.

In step 1814 virtual store tool 405 determines whether the tool received information identifying a virtual item 320 that is not included in product list 1704. As an example, in certain embodiments, receiving information identifying a virtual item 320 that is not included in product list 1704 includes receiving information associated with dragging and dropping the virtual item 320 from a virtual rack 230, displayed in region 905 of display 410, to virtual shopping cart 420. For example, user 120 may drag and drop the virtual item 320 from virtual rack 230 to virtual shopping cart 420 in response to viewing, in camera segment 1702, customer 105 a selecting the virtual item 320. If, in step 1814 virtual store tool 405 determines that the tool received information identifying a virtual item 320 that is not included in product list 1704, in step 1816 virtual store tool 405 adds the virtual item 320 to virtual shopping cart 420. Method 1800 then proceeds to step 1822. If, in step 1814 virtual store tool 405 determines that the tool did not receive information identifying a virtual item 320 that is not included in product list 1704, method 1800 proceeds directly to step 1822.

In step 1822 virtual store tool 405 determines whether any additional virtual items 320 are present in product list 1704. If, in step 1822 virtual store tool 405 determines that one or more additional virtual items 320 are present in product list 1704, method 1800 returns to step 1806. If, in step 1822 virtual store tool 405 determines that no additional virtual items 320 are present in product list 1704, in step 1824 virtual store tool 405 determines whether the tool received information identifying a virtual item 320 that is not included in product list 1704. As an example, in certain embodiments, receiving information identifying a virtual item 320 that is not included in product list 1704 includes receiving information associated with dragging and dropping the virtual item 320 from a virtual rack 230, displayed in region 905 of display 410, to virtual shopping cart 420. For example, user 120 may drag and drop the virtual item 320 from virtual rack 230 to virtual shopping cart 420 in response to viewing, in camera segment 1702, customer 105 a selecting the virtual item 320. If, in step 1824 virtual store tool 405 determines that the tool received information identifying a virtual item 320 that is not included in product list 1704, in step 1826 virtual store tool 405 adds the virtual item 320 to virtual shopping cart 420. Method 1800 then proceeds to step 1828. If, in step 1824 virtual store tool 405 determines that the tool did not receive information identifying a virtual item 320 that is not included in product list 1704, method 1800 proceeds directly to step 1828. In step 1828 virtual store tool 405 determines whether the shopping session has ended. If, in step 1828 virtual store tool 405 determines that the shopping session has not ended, method 1800 returns to step 1824. If, in step 1828 virtual store tool 405 determines that the shopping session has ended, method 1800 ends.

Modifications, additions, or omissions may be made to method 1800 depicted in FIG. 18. Method 1800 may include more, fewer, or other steps. For example, steps may be performed in parallel or in any suitable order. While discussed as virtual store tool 405 (or components thereof) performing the steps, any suitable component of system 400, such as device(s) 115 for example, may perform one or more steps of the method.

VIII. Evaluation of a Refund Request through Virtual Emulation of a Prior Shopping Session

As described above, a customer 105 may be charged for items that he/she selected during a shopping session in a physical store 100 based on: (1) a virtual emulation of the shopping session, using virtual store tool 405; (2) an algorithmic determination, based on information gathered by sensors 498 in the physical store during the shopping session, of items selected by the customer; or (3) a combination of virtual emulation and algorithmic determinations. Regardless of the manner by which the charges are determined, in certain embodiments, customer 105 may receive a receipt 1465 detailing the charges that were applied to his/her account. Customer 105 may dispute one or more of these charges, for a variety of reasons. As an example, in certain embodiments where virtual store tool 405 was used to generate the charges, an error may have occurred in the virtual emulation process such that one or more of the items for which customer 105 was charged were not actually selected by the customer during his/her shopping session and/or customer 105 did not select the quantity of one of more of the items for which customer 105 was charged. As another example, in certain embodiments where algorithmic shopping cart 1420 was used to generate the charges, customer 105 may not have actually selected one or more of the items that algorithm 488 determined customer 105 selected, and/or a quantity that algorithm 488 assigned to one or more of the items included in algorithmic shopping cart 1420 may not be accurate. As a further example, in certain embodiments, the charges may be accurate but customer 105 may have misremembered the items that he/she selected during his/her shopping session.

FIGS. 19A through 19C present an example illustrating the use of virtual store tool 405 to process a refund request 1902 a submitted by a customer 105 a. As illustrated in FIG. 19A, refund request 1902 a is a request for a refund of the purchase price of physical item 315 w that was charged to an account belonging to customer 105 a in response to a prior shopping session in physical store 100. While illustrated in FIG. 19A as corresponding to a request for the refund of the purchase price of a single physical item 315 w, virtual store tool 405 may be used to process requests for refunds of the purchase price of any number of physical items.

a. Receiving a Refund Request

Customer 105 a may submit refund request 1902 a to virtual store tool 405 in any suitable manner. As an example, in certain embodiments, customer 105 a submits refund request 1902 a to virtual store tool 405 through a webpage. As another example, in certain embodiments, customer 105 a submits refund request 1902 a to virtual store tool 405 using his/her mobile device 125 a. For example, in certain embodiments, receipt 1465 a includes an interactive link through which customer 105 a may submit refund request 1902 a. In certain such embodiments, in response to receiving receipt 1465 a on device 125 a, customer 105 a uses device 125 a to interact with the link to submit refund request 1902 a. In some embodiments, customer 105 a may use an application installed on device 125 a to submit refund request 1465 a.

In response to receiving refund request 1465 a, virtual store tool 405 places refund request 1465 a in queue 1906, stored in memory 440. Virtual store tool 405 uses queue 1906 to help ensure that the refund requests it receives are processed in a timely manner. In particular, as illustrated in FIG. 19B, any number of customers 105 a through 105 n may submit refund requests 1902 a through 1902 o to virtual store tool 405. In certain embodiments, there may not be any users 120 a through 120 m who are available to process the incoming refund requests 1902 a through 1902 o as they are received. For example, users 120 a through 120 m may not be available to process an incoming refund request 1902 a because they are virtually emulating shopping sessions and/or processing earlier-received refund requests. Virtual store tool 405 uses queue 1906 to help ensure that when a user 120 becomes available, he/she is provided with the earliest-received refund request that has not yet been processed, thereby helping to ensure that refund requests 1902 a through 1902 o are processed in the order that they are received by virtual store tool 405.

As an example of the manner by which virtual store tool 405 uses queue 1906 to assign refund requests 1902 a through 1902 o to users 120 a through 120 m, as illustrated in FIG. 19B, a first refund request 1902 a, submitted by customer 105 a, is stored in a first slot 1908 a of queue 1906, a second refund request 1902 c is stored in a second slot 1908 b of queue 1906, and an n-th refund request 1902 n is stored in an n-th slot 1908 n of queue 1906. In response to receiving refund request 1902 o from customer 105 b, virtual store tool 405 stores refund request 1902 o at the end of queue 1906, in slot 19080. In response to receiving indication 1909 a from user 120 a, indicating that user 120 a is available to process a refund request, virtual store tool 405 assigns the refund request 1902 a that is stored in the first slot 1908 a of queue 1906 to user 120 a. For example, virtual store tool 405 generates a graphical user interface on device 115 a of user 120 a, for use by user 120 a in processing refund request 1902 a, as described in further detail below, in the discussion of FIG. 19C. Virtual store tool 405 then moves each refund request 1902 b through 1902 o that remains in queue 1906 up one slot, such that refund request 1902 b will be the next refund request assigned to a user 120, once a user 120 indicates that he/she is available to process a refund request. Each refund request 1902 a through 1902 o submitted by customers 105 may include any suitable information. For example, as illustrated in FIG. 19A, in certain embodiments, in addition to an identification of the physical item(s) 315 w for which customer 105 a is submitting refund request 1902 a, the request may also include a description 1904 of the reason customer 105 a submitted the refund request. For instance, description 1904 may indicate: (1) that the customer did not select a specific item 315 w; (2) that the customer did not select a specific item 315 w but did select a different item for which he/she was not charged; (3) that the customer was charged for a greater quantity of an item 315 w than what he/she selected; and/or (4) any other suitable information that may aid virtual store tool 405 in processing the customer's refund request. In certain embodiments, description 1904 is a free-form textual description submitted by customer 105 a. For example, in some embodiments, customer 105 a may enter description 1904 in a text box displayed on his/her mobile device 125 a. In certain embodiments, description 1904 is one of a number of standard statements that customer 105 a may choose from amongst. For example, in some embodiments, customer 105 a may enter description 1904 by selecting from a number of options displayed as a drop-down list on his/her mobile device 125 a.

In some embodiments, refund request 1902 a submitted by customer 105 a includes information identifying the shopping session associated with the refund request and/or a portion of the shopping session relevant to the refund request. As an example, in certain embodiments, the refund request includes the start and/or end times of the shopping session. For example, refund request 1902 a may indicate that the shopping session began at 4:15:05 pm on Jun. 26, 2020 and lasted until 4:17:55 pm that same day. As another example, in certain embodiments, the refund request includes an identification number associated with the shopping session, which virtual store tool 405 may use to determine the start and/or end times of the shopping session. For example, virtual store tool 405 may use the identification number to look up the start and/or end times of the shopping session in the set of shopping session identification information 486 stored in database 482. As a further example, in certain embodiments, refund request includes an identification number associated with the shopping session, which virtual store tool 405 may use, in conjunction with the items included in the refund request, to determine portions of the shopping session associated with the refund request. For example, virtual store tool 405 may use the identification number, along with the items included in the refund request, to look up the times of the shopping session associated with the items (e.g., the times at which the items were added to a virtual shopping cart during a virtual emulation of the shopping session and/or the times at which algorithm 488 determined that the items were selected during the shopping session) in the set of shopping session identification information 486 stored in database 482.

b. Processing a Refund Request

In response to assigning a user 120 a a refund request 1902 a, virtual store tool 405 may generate a graphical user interface for display on display 410 of device 115 a, which user 120 a may use to process the refund request. FIG. 19C presents an example of a graphical user interface 1900 that may be used by user 120 a to process refund request 1902 a. In certain embodiments, graphical user interface 1900 may be used by a user 120 to review a prior shopping session of customer 105 in physical store 100 and/or a portion of a prior shopping session of customer 105, in order to process the refund request submitted by the customer.

i. Displaying Information Received in the Refund Request

As illustrated in FIGS. 19A and 19C, in response to receiving a refund request 1902 a, virtual store tool 405 may display information associated with the request on graphical user interface 1900. This information may aid user 120 a in processing the refund request. For example, virtual store tool 405 may display description 1904 received from customer 105 a in region 1910 of graphical user interface 1900 and/or an identification of the item(s) 320 w for which customer 105 a is requesting the refund in region 1914 of graphical user interface 1900. As described above description 1904 may include text submitted by customer 105 a detailing the reason that he/she submitted the refund request.

ii. Video-Based Review of the Shopping Session Associated with the Refund Request

A. Identifying and Displaying Video of the Shopping Session

In response to receiving refund request 1902 a, virtual store tool 405 additionally identifies and displays historical video segments 1922 a through 1922 f on graphical user interface 1900. Historical video segments 1922 a through 1922 f correspond to segments of video captured by layout cameras 490 and/or rack cameras 495 during the shopping session of customer 105 a that is associated with refund request 1902 a. In certain embodiments, historical video segments 1922 a through 1922 f correspond to segments of video captured by layout cameras 490 and/or rack cameras 495 during the full duration of the shopping session of customer 105 a. In some embodiments, historical video segments 1922 a through 1922 f correspond to segments of video captured by layout cameras 490 and/or rack cameras 495 during a portion of the shopping session of customer 105 a.

Virtual store tool 405 may obtain video segments 1922 a through 1922 f in any suitable manner. As an example, in certain embodiments virtual store tool 405 may use information provided in refund request 1902 a to obtain video segments 1922 a through 1922 f. As an example, in certain embodiments refund request 1902 a includes the start and/or end times of the shopping session. In some embodiments, the refund request includes an identification number for the shopping session, which may be used to look up the start and/or end times of the shopping session in the set of shopping session identification information 486 stored in database 482. Once virtual store tool 405 has obtained the start and/or end times of the shopping session, the tool may use these times to extract video segments 1922 a through 1922 f from videos 484 stored in database 482. For example, virtual store tool 405 may store a portion of video 484 a beginning at a timestamp corresponding to the start time and ending at a timestamp corresponding to the end time as video segment 1922 a. Virtual store tool 405 may generate each of video segments 1922 b through 1922 f in a similar manner. As another example, in certain embodiments, refund request 1902 a includes an identification number for the shopping session, which virtual store tool 405 may use, in conjunction with the item(s) 315 w for which refund request 1902 a was submitted, to identify portion(s) of the shopping session relevant to evaluating whether or not customer 105 selected item(s) 315 w. For example, virtual store tool 405 may use the identification number, along with the item(s) 315 w included in the refund request, to look up the time(s) of the shopping session associated with the item(s) (e.g., the time(s) at which the item(s) were added to a virtual shopping cart during a virtual emulation of the shopping session and/or the time(s) at which algorithm 488 determined that the item(s) were selected during the shopping session) in the set of shopping session identification information 486 stored in database 482. Once virtual store tool 405 has obtained the time(s) of the shopping session, the tool may use these time(s) to extract video segments 1922 a through 1922 f from videos 484 stored in database 482. For example, virtual store tool 405 may store, as video segment 1922 a, a portion of video 484 a beginning at a timestamp occurring a set time interval before the time of the shopping session that is associated with customer 105 a selecting item 315 w (as determined by algorithm 488 and/or a virtual emulation of the customer's shopping session using virtual store tool 405) and ending at a timestamp occurring a set time interval after the time of the shopping session that associated with the customer selecting item 315 w as video segment 1922 a. Virtual store tool 405 may generate each of video segments 1922 b through 1922 f in a similar manner.

B. Using Video of the Shopping Session to Process the Refund Request

User 120 a may use graphical user interface 1900, generated by virtual store tool 405, to process a refund request 1902 a submitted by a customer 105 a by reviewing video segments 1922 a through 1922 f to determine whether any of these video segments include video of customer 105 a selecting one or more physical items for which the customer submitted the refund request. For example, with respect to the example illustrated in FIGS. 19A and 19C, user 120 a may review video segments 1922 a through 1922 f to determine whether any of these video segments depict customer 105 a selecting physical item 315 w (which corresponds to virtual item 320 w).

Virtual store tool 405 may provide graphical user interface 1900 with any of a number of features that may aid user 120 a in processing refund request 1902 a. As a first example, in certain embodiments, and in a similar manner as that described in Section III above for camera feed segments 415, virtual store tool 405 may assign a common slider bar 705 to each of video segments 1922 a through 1922 f. Slider 710 on slider bar 705 is configured to control the playback process of the video segment. In certain embodiments, the playback of each video segment 1922 a through 1922 f is synchronized with that of the other video segments 1922 a through 1922 f, such that an adjustment of the slider 710 on any of the copies of slider bar 705 leads to a corresponding adjustment of the playback progress of all of the displayed video segments 1922 a through 1922 f.

In certain embodiments, in order to further aid user 120 a in reviewing the shopping session associated with refund request 1902 a, slider bar 705 may include one or more markers 1916. For example, as illustrated in FIG. 19C, slider bar 705 may include marker 1916, located at a marker position on slider bar 705 and corresponding to a marker playback time. Each marker 1916 may be associated with an event related to an item 315 w/320 w that is a subject of the refund request. For example, as illustrated in FIG. 19C, marker 1916 may be associated with an event related to item 320 w. As an example of an event that marker 1916 may be associated with, in certain embodiments in which virtual store tool 405 was previously used to emulate the shopping session of customer 105 a and generate the receipt 1465 a associated with the customer's refund request, the marker playback time associated with marker 1916 may correspond to the playback time of camera segments captured by layout cameras 490 and/or rack cameras 495, during the previous virtual emulation session, at which point a user added virtual item 320 w to virtual shopping cart 420. As another example, the marker playback time associated with marker 1916 may indicate the historical time at which algorithm 488 added physical item 315 w to algorithmic shopping cart 1420 during customer 105′s previous shopping session. Accordingly, marker 1916 may indicate to user 120 a the general time of video segments 1922 a through 1922 f around which time user 120 a might expect to view customer 105 a selecting item 315 w, if customer 105 did in fact select item 315 w.

As a second example of a feature that may aid user 120 a in processing refund request 1902 a, in order to aid user 120 a in observing customer 105 a on video segments 1922 a through 1922 f, in certain embodiments, user 120 a can choose to display any of video segments 1922 a through 1922 f as enlarged video segment 1924. For example, as illustrated in FIG. 19C, user 120 a may choose to display historical video segment 1922 c as enlarged video segment 1924. User 120 a can select one of video segments 1922 a through 1922 f to display as enlarged video segment 1924 in any suitable manner. For example, in certain embodiments, user 120 a can click on a given video segment 1922 a through 1922 f to instruct virtual store tool 405 to display the video segment as enlarged video segment 1924.

As a third example of a feature that may aid user 120 a in processing refund request 1902 a, in certain embodiments, in order to aid user 120 a in observing customer 105 a ′s interactions with a given physical rack 215 a, virtual store tool 405 may display a video segment captured by a rack camera 495, directed at the given physical rack 215 a, as enlarged video segment 1924. As an example, in certain embodiments, virtual store tool 405 may display a video segment captured by a rack camera 495 directed at the physical rack 210 a that stores a physical item 315 w that is a subject of customer 105 a's refund request 1902 a. For instance, in certain embodiments, virtual store tool 405 may automatically display, as enlarged video segment 1924, a video segment captured by a rack camera 495 directed at the physical rack 210 a that stores physical item 315 w. In some embodiments, virtual store tool 405 may display, as enlarged video segment 1924, a video segment captured by a rack camera 495 directed at the physical rack 210 a that stores physical item 315 w, in response to user 120 a selecting (e.g., clicking on, double-clicking on, and/or any other suitable method of selecting) the corresponding virtual item 320 w from region 1914 of graphical user interface 1900.

As a fourth example of a feature that may aid user 120 a in processing refund request 1902 a, in certain embodiments, virtual store tool 405 may highlight a virtual rack 230 a, corresponding to a physical rack 210 a that stores a first physical item 315 w that is a subject of customer 105 a's refund request. Highlighting virtual rack 230 a may aid user 120 a in identifying which video segments of the shopping session he/she should review. For example, in response to observing that virtual rack 230 a is highlighted, user 120 a may choose to display a video segment captured by a rack camera 495 that is directed at the corresponding physical rack 210 a, by selecting from amongst the available rack cameras 495 that were directed at physical rack 210 a. For instance, in certain embodiments, virtual store tool 405 may display icons 1706 a/b on virtual layout 205, where the locations of icons 1706 a/b on virtual layout 205 emulate the physical locations of rack cameras 495 within physical store 100. In response to user 120 a selecting an icon 1706 a/b (e.g., clicking on the icon, double-clicking on the icon, and/or any other suitable method of selecting the icon), virtual store tool 405 may display, as enlarged video segment 1924, a video segment captured by the rack camera 495 corresponding to the icon 1706 a/b. Virtual store tool 405 may highlight a virtual rack 230 a that stores a virtual item 320 w that is a subject of customer 105 a's refund request 1902 a in any suitable manner. As an example, in certain embodiments, virtual store tool 405 may automatically highlight virtual rack 230 a in response to displaying graphical user interface 1900 on display 410. As another example, virtual store tool 405 may highlight virtual rack 230 a in response to user 120 a selecting (e.g., clicking on, double-clicking on, and/or any other suitable method of selecting) a virtual item 320 w from region 1920 of graphical user interface 1900, which is stored on virtual rack 230 a.

User 120 a may accept or reject a customer's refund request 1902 a based on the user's review of video segments 1922 a through 1922 f and/or 1924. As an example, in certain embodiments, user 120 a may observe, in video segments 1922 a through 1922 f and/or 1924, customer 105 a selecting a physical item 315 w that is a subject of customer 105 a's refund request 1902 a. Accordingly, user 120 a may decline customer 105 a's refund request for that physical item 315 w. User 120 a may decline customer 105 a's refund request 1902 a in any suitable manner. For example, in certain embodiments, user 120 a may select decline button 1918 to decline the refund request. In response to user 120 a declining the refund request, virtual store tool 405 may transmit a message 1926 (illustrated in FIG. 19A) to customer 105 a indicating that his/her refund request 1902 a has been denied. For example, in certain embodiments, virtual store tool 405 may transmit a message 1926 to device 125 a indicating that refund request 1902 a has been denied. In certain embodiments in which refund request 1902 a includes a request for a refund of the purchase price of more than one item, user 120 a may decline all of a portion of the refund request. For example, in response to reviewing customer 105 a selecting physical item 315 w in video segments 1922 a through 1922 f and/or 1924, user 120 a may decline a first portion of the refund request associated with a first item 315 w, but nevertheless accept a second portion of the refund request associated with a second item.

As another example, in some embodiments, user 120 a may determine, by observing video segments 1922 a through 1922 f and/or 1924, that customer 105 a did not select a physical item 315 w that is a subject of customer 105 a ′ s refund request 1902 a. Accordingly, user 120 a may accept customer 105 a's refund request for that physical item 315 w. User 120 a may accept customer 105 a's refund request in any suitable manner. For example, in certain embodiments, user 120 a may select accept button 1920 to accept the refund request. In response to user 120 a accepting customer 105 a's refund request, virtual store tool 405 may credit an account belonging to customer 105 a with the purchase price of the physical item 315 w. Virtual store tool 405 may also transmit a message 1926 (illustrated in FIG. 19A) to customer 105 a indicating that his/her refund request has been accepted. For example, in certain embodiments, virtual store tool 405 may transmit a message 1926 to device 125 a indicating that refund request 1902 a has been accepted.

iii. Image-Based Review of the Shopping Session Associated with the Refund Request

In certain embodiments, in addition to or instead of reviewing video segments 1922 a through 1922 f and/or 1924, user 120 a may review an image 1710 of customer 105 a that was captured as the customer exited physical store 100, in order to process a refund request 1902 a submitted by customer 105 s. Image 1710 may have been captured in any suitable manner. For example, in certain embodiments in which physical store 100 includes turnstiles 510 to control the entry/exit of persons 105 into/out of the store (as depicted in FIG. 5A), physical store 100 may include a camera configured to capture an image 1710 of customer 105 a as customer 105 a exits through a turnstile 510. In certain embodiments, image 1710 is stored in shopping session identification information 486 in database 482. In response to customer 105 a submitting a refund request 1902 a that includes information identifying the shopping session, virtual store tool 405 may access database 482 and use this identification information to locate image 1710. Virtual store tool 405 may then display image 1710 on graphical user interface 1900. User 120 a may review image 1710 to quickly determine whether customer 105 a exited physical store 100 with any of the physical items that are the subjects of the customer's refund request. For example, as illustrated in FIG. 19, user 120 a may review image 1710 to determine that customer 105 a exited physical store 100 with physical item 315 j but not physical item 315 w. Accordingly, user 120 a may accept customer 105 a's refund request for a refund of the purchase price of physical item 315 w.

c. Method for Processing a Refund Request

FIG. 20 presents a flowchart (described in conjunction with elements of FIGS. 19A through 19C) illustrating the manner by which virtual store tool 405 may be used to process a refund request 1902 a submitted by a customer 105 a. In step 2002 virtual store tool 405 receives a request 1902 a for a refund of the purchase price(s) of one or more physical items 315 w that were charged to an account belonging to customer 105 a in response to a prior shopping session conducted by customer 105 a in physical store 100. In step 2004 virtual store tool 405 identifies the shopping session associated with the refund request 1902 a. For example, in certain embodiments, the refund request submitted by customer 105 a includes information identifying the shopping session associated with the refund request. Accordingly, virtual store tool 405 may access this information to identify the shopping session associated with the refund request. As an example, in certain embodiments, refund request 1902 a includes the start and/or end times of the shopping session. For example, the refund request may indicate that the shopping session began at 4:15:05 pm on Jun. 26, 2020 and lasted until 4:17:55 pm that same day. As another example, in certain embodiments, refund request 1902 a includes an identification number associated with the shopping session. Virtual store tool 405 may use this identification number to determine the start and/or end times of the shopping session. For example, virtual store tool 405 may use the identification number to look up the start and/or end times of the shopping session in the set of shopping session identification information 486 stored in database 482. As a further example, in certain embodiments, refund request 1902 a includes an identification number associated with the shopping session, which virtual store tool 405 may use, in conjunction with the items included in the refund request, to determine portions of the shopping session associated with the refund request. For example, virtual store tool 405 may use the identification number, along with the items included in the refund request, to look up the times of the shopping session associated with the items (e.g., the times at which the items were added to a virtual shopping cart during a virtual emulation of the shopping session and/or the times at which algorithm 488 determined that the items were selected during the shopping session) in the set of shopping session identification information 486 stored in database 482.

In step 2006 virtual store tool 405 generates and displays historical video segments 1922 a through 1922 f and/or 1924 on graphical user interface 1900. Historical video segments 1922 a through 1922 f and/or 1924 correspond to segments of video captured by layout cameras 490 and/or rack cameras 495 during the shopping session of customer 105 a that is associated with the refund request. Virtual store tool 405 may obtain video segments 1922 a through 1922 f and/or 1924 in any suitable manner. As an example, in certain embodiments virtual store tool 405 uses the start and/or end times associated with the shopping session, which were obtained from the refund request, to extract video segments 1922 a through 1922 f and/or 1924 from videos 484 a through 484 h stored in database 482. For example, virtual store tool 405 may store a portion of video 484 a beginning at a timestamp corresponding to the start time and ending at a timestamp corresponding to the end time as video segment 1922 a. Virtual store tool 405 may generate each of video segments 1922 b through 1922 f and/or 1924 in a similar manner. As another example, in certain embodiments, virtual store tool 405 uses the time(s) of the shopping session associated with the item(s) included in refund request 1902 a to extract video segments 1922 a through 1922 f and/or 1924 from videos 484 stored in database 482. For example, virtual store tool 405 may store, as video segment 1922 a, a portion of video 484 a beginning at a timestamp occurring a set time interval before the time of the shopping session that is associated with customer 105 a selecting item 315 w (as determined by algorithm 488 and/or a virtual emulation of the customer's shopping session using virtual store tool 405) and ending at a timestamp occurring a set time interval after the time of the shopping session that associated with the customer selecting item 315 w as video segment 1922 a. Virtual store tool 405 may generate each of video segments 1922 b through 1922 f and/or 1924 in a similar manner.

In step 2008 virtual store tool 405 selects a first physical item 315 w (corresponding to virtual item 320 w) from the refund request. In step 2010 virtual store tool 405 determines whether customer 105 a selected physical item 315 w during the shopping session associated with the refund request. For example, in certain embodiments, virtual store tool 405 receives an indication from user 120 a that user 120 a either observed customer 105 a selecting physical item 315 w on video segments 1922 a through 1922 f and/or 1924 or did not observe customer 105 a selecting physical item 315 w on video segments 1922 a through 1922 f and/or 1924. If, in step 2010 virtual store tool 405 determines that customer 105 a selected physical item 315 w during the shopping session, in step 2012 virtual store tool 405 declines to process the refund request associated with physical item 315 w. In certain embodiments, virtual store tool 405 may transmit a message 1926 to customer 105 a informing customer 105 a that his/her refund request has been denied. Method 2000 then proceeds to step 2016. If, in step 2010 virtual store tool 405 determines that customer 105 a did not select physical item 315 w during the shopping session, in step 2014 virtual store tool 405 processes the refund request associated with physical item 315 w. For example, in certain embodiments, virtual store tool 405 may credit an account belonging to customer 105 a with the purchase price of the physical item 315 w. In some embodiments, virtual store tool 405 may also transmit a message 1926 to customer 105 a indicating that his/her refund request 1902 a has been accepted. Method 2000 then proceeds to step 2016.

In step 2016 virtual store tool 405 determines whether customer 105 a's refund request 1902 a includes any additional items 315 w. If, in step 2016 virtual store tool 405 determines that customer 105 a's refund request includes one or more additional items, method 2000 returns to step 2008.

Modifications, additions, or omissions may be made to method 2000 depicted in FIG. 20. Method 2000 may include more, fewer, or other steps. For example, steps may be performed in parallel or in any suitable order. While discussed as virtual store tool 405 (or components thereof) performing the steps, any suitable component of system 400, such as device(s) 115 for example, may perform one or more steps of the method.

IX. Selective Use of Virtual Emulation as a Means for Verifying Algorithmic Shopping Carts

As described in Section VI above, in certain embodiments, virtual store tool 405 may be used in conjunction with an external algorithm 488 that is configured to model a customer's shopping session in physical store 100, based on inputs received from sensors 498 located within the store. For example, virtual store tool 405 may be used to verify the determinations made by algorithm 488 of the items that customer 105 selected during the shopping session and/or to help improve the accuracy of algorithm 488 by providing feedback to algorithm 488. In certain embodiments, using virtual store tool 405 to verify each determination made by algorithm 488 may not be feasible. For example, significant processing resources may be expended by verifying each determination made by algorithm 488. Accordingly, rather than verifying each determination made by algorithm 488, it may be desirable to selectively verify determinations made by algorithm 488. For example, it may be desirable to verify those determinations made by algorithm 488 for which there exists a reasonable probability that the algorithmic determination is incorrect, while declining to verify those determinations made by algorithm 488 for which there exists a high probability that the algorithmic determination is correct.

a. Selective Verification System Overview

FIG. 21 presents an example system 2100 configured to use virtual store tool 405 to verify those determinations made by algorithm 488, for which the probability that the algorithmic determination is incorrect is greater than a threshold. In particular, cart review assistant 2102 of system 2100 is configured to determine whether or not to invoke virtual store tool 405 to verify all or a portion of an algorithm shopping cart 1420 (which includes items that algorithm 488 determined that customer 105 selected during a shopping session in physical store 100), based on the probability that one or more of the items included in the algorithmic shopping cart 1420 is incorrect. As can be seen by a comparison between system 2100 presented in FIG. 21 and system 400 presented in FIG. 4, system 2100 includes many of the same components as system 400—namely virtual store tool 405, network 430, external system 485, layout cameras 490, rack cameras 495, and sensors 498. Accordingly, in the discussion that follows, it is assumed that the features and functions of these shared components include any of those features/functions presented in Sections I through VII above.

As illustrated in FIG. 21, in addition to virtual store tool 405, network 430, external system 485, layout cameras 490, rack cameras 495, and sensors 498, system 2100 includes cart review assistant 2102 and, in certain embodiments, database 2104. Cart review assistant 2102 includes a processor 2108 and a memory 2110. This disclosure contemplates processor 2108 and memory 2110 being configured to perform any of the functions of cart review assistant 2102 described herein. For example, memory 2110 may include a set of instructions 2124 that, when executed by processor 2108, perform one or more of the functions of cart review assistant 2102 described herein. Generally, cart review assistant 2102 is configured to apply machine learning algorithm 2112 to sensor inputs 2122, to determine whether or not all or a portion of algorithmic shopping cart 1420, generated by external system 485, is likely to include errors (i.e., likely to not accurately reflect the items actually selected by customer 105 during his/her shopping session) and is therefore a good candidate for verification by virtual store tool 405. For example, machine learning algorithm 2112 may determine whether the probability that algorithmic shopping cart 1420 includes an error is greater than a threshold. In certain embodiments, cart review assistant 2102 is also configured use training data 2106, stored in database 2104, to train machine learning algorithm 2112 to identify virtual shopping carts 1420 as candidates for verification. In some embodiments, cart review assistant 2102 is further configured to update/refine machine learning algorithm 2112 based on feedback 2116 received from virtual store tool 405 for prior decisions made by the algorithm, in order to improve the accuracy of machine learning algorithm 2112. These functions of cart review assistant 2102 are described in further detail below, and in particular, in the discussion of FIGS. 22 and 23.

Processor 2108 is any electronic circuitry, including, but not limited to central processing units (CPUs), graphics processing units (GPUs), microprocessors, application specific integrated circuits (ASIC), application specific instruction set processor (ASIP), and/or state machines, that communicatively couples to memory 2110 and controls the operation of cart review assistant 2102. Processor 2108 may be 8-bit, 16-bit, 32-bit, 64-bit or of any other suitable architecture. Processor 2108 may include an arithmetic logic unit (ALU) for performing arithmetic and logic operations, processor registers that supply operands to the ALU and store the results of ALU operations, and a control unit that fetches instructions from memory and executes them by directing the coordinated operations of the ALU, registers and other components. Processor 2108 may include other hardware and software that operates to control and process information. Processor 2108 executes software stored on memory to perform any of the functions described herein. Processor 2108 controls the operation and administration of cart review assistant 2102 by processing information received from virtual store tool 405, network 430, external system 485, layout cameras 490, rack cameras 495, sensors 498, and/or database 2104. Processor 2108 may be a programmable logic device, a microcontroller, a microprocessor, any suitable processing device, or any suitable combination of the preceding. Processor 2108 is not limited to a single processing device and may encompass multiple processing devices.

Memory 2110 may store, either permanently or temporarily, data, operational software, or other information for processor 2108. Memory 2110 may include any one or a combination of volatile or non-volatile local or remote devices suitable for storing information. For example, memory 2110 may include random access memory (RAM), read only memory (ROM), magnetic storage devices, optical storage devices, or any other suitable information storage device or a combination of these devices. The software represents any suitable set of instructions, logic, or code embodied in a computer-readable storage medium. For example, the software may be embodied in memory 2110, a disk, a CD, or a flash drive. In particular embodiments, the software may include an application executable by processor 2108 to perform one or more of the functions described herein.

Additionally, in certain embodiments, memory 2110 may store machine learning algorithm 2112. Machine learning algorithm 2112 is any algorithm configured to determine whether algorithmic shopping cart 1420 is a good candidate for verification by virtual store tool 405 (e.g., whether algorithmic shopping cart 1420 likely includes errors). Machine learning algorithm 2112 may determine whether or not algorithmic shopping cart 1420 is a good candidate for verification based on sensor inputs 2122 that were received from sensors 498 located in physical store 100, and which were used by algorithm 488 to generate algorithmic shopping cart 1420. In certain embodiments, machine learning algorithm 2112 is configured to categorize an algorithmic shopping cart 1420 as either a good candidate for verification or not a good candidate for verification. Machine learning algorithm 2112 may base its categorization of an algorithmic shopping cart 1420 as either a good candidate or a bad candidate on the probability that the algorithmic shopping cart includes one or more errors. For instance, machine learning algorithm 2112 may categorize an algorithmic shopping cart 1420 as a good candidate for verification by virtual store tool 405 where the probability that the algorithmic shopping cart includes at least one error is greater than a certain threshold.

In some embodiments, machine learning algorithm 2112 may further categorize those algorithmic shopping carts 1420 that are good candidates for verification according to the probable type and/or cause of the error(s) likely present in the algorithmic shopping cart 1420. As an example, machine learning algorithm 2112 may categorize an algorithmic shopping cart 1420 that is a good candidate for verification as likely: (1) including one or more physical items 315 that customer 105 did not select; (2) not including one or more physical items 315 that customer 105 did select; (3) including an incorrect quantity of one or more physical items 315 that customer 105 selected; and/or (4) including any other type of error that may be present in algorithmic shopping cart 1420. As another example, machine learning algorithm 2112 may categorize an algorithmic shopping cart 1420 that is a good candidate for verification as likely including one or more errors because: (1) one or more servers within external system 485 likely was not functioning properly; (2) external system 485 likely was not properly recording information received from one or more weight sensors 1300 (illustrated in FIGS. 13B through 13D); (3) one or more weight sensors 1300 likely generated a false positive; (4) algorithm 488 likely generated one or more incorrect item counts; (5) algorithm 488 likely assigned one or more items 315 to the wrong customer 105; (6) algorithm 488 likely lost track of customer 105; and/or (7) any other likely cause of the error(s) within algorithmic shopping cart 1420.

Machine learning algorithm 2112 may be configured to classify all or a portion of an algorithmic shopping cart 1420 as a good candidate for verification (e.g., as likely including one or more errors). As an example, consider an algorithmic shopping cart 1420 that includes two items—first item 315 a and second item 315 b. Algorithm 488 may have added first item 315 a to algorithmic shopping cart 1420 based on a first subset of sensor inputs 2122 received from sensors 498 during a first portion of a shopping session and added second item 315 b to algorithmic shopping cart 1420 based on a second subset of sensor inputs 2122 received from sensors 498 during a second portion of the shopping session. In certain embodiments, machine learning algorithm 2112 may be configured to classify first item 315 a, based on the first subset of sensor inputs 2122, as a good candidate for verification by virtual store tool 405 (e.g., likely incorrect) and to classify second item 315 b, based on the second subset of sensor inputs 2122, as not a good candidate for verification by virtual store tool 405 (e.g., likely correct). In some embodiments, machine learning algorithm 2112 may be configured to classify the full algorithmic shopping cart 1420 (e.g., both first item 315 a and second item 315 b) as a good candidate for verification by virtual store tool 405 (e.g., likely including one or more errors), based on the full set of sensor inputs 2122. Splitting an algorithmic shopping cart 1420 into portions and determining whether or not each portion is a good candidate for verification by virtual store tool 405 may be desirable to reduce the computational costs associated with using virtual store tool 405 to verify algorithmic shopping cart 1420; it may be more efficient to use virtual store tool 405 to review a portion of a shopping session in order to verify a portion of an algorithmic shopping cart 1420 rather than the reviewing the entire shopping session.

Machine learning algorithm 2112 may be any suitable machine learning algorithm. As a first example, in certain embodiments, machine learning algorithm 2112 is a supervised learning algorithm. For example, machine learning algorithm 2112 may be a supervised learning algorithm that has been trained, using training data 2106, to determine whether or not an algorithmic shopping cart 1420 is a good candidate for verification based on sensor inputs 2122. Sensor inputs 2122 correspond to inputs that were received from sensors 498 located in physical store 100 and were used by algorithm 488 to generate algorithmic shopping cart 1420. As described in detail below, in certain embodiments, training data 2106 may include sets of historical sensor inputs 2118 a through 2118 n, each of which is assigned a corresponding label 2120 a through 2120 n labeling whether or not an algorithmic shopping cart generated based on the historical sensor inputs was a good candidate for verification (e.g., whether or not the algorithmic shopping cart likely included one or more errors). For example, label 2120 a may indicate that the algorithmic shopping cart generated using historical sensor inputs 2118 a was inaccurate, and therefore was a good candidate for verification by virtual store tool 405. Similarly, label 2120 b may indicate that the algorithmic shopping cart generated using historical sensor inputs 2118 b was accurate and therefore was not a good candidate for verification by virtual store tool 405. Machine learning algorithm 2112 may be trained, using training data 2106, to identify patterns within the sets of historical sensor inputs 2118 a through 2118 n and corresponding labels 2120 a through 2120 n, such that machine learning algorithm 2112 may assign a label to new sensor inputs 2122, based on these learned patterns.

As a specific example of a supervised machine learning algorithm 2112 that may be used by cart review assistant 2102 to identify those algorithmic shopping carts 1420 that are good candidates for verification by virtual store tool 405, in certain embodiments, machine learning algorithm 2112 is a neural network algorithm. For example, machine learning algorithm 2112 may be a neural network that includes an input layer of nodes, one or more hidden layers of nodes, and an output layer of nodes. The input layer of nodes is configured to take, as input, sensor inputs 2122 that are associated with a given shopping session of a customer 105 within physical store 100, and to transfer these inputs to the first hidden layer of nodes. Each node within the hidden layer(s) of the neural network receives a certain number of inputs from the previous layer of nodes and is associated with a set of weights, one for each received input. Each hidden layer node is configured to calculate a weighted sum of the inputs it receives, add a bias, and execute an activation function. The output of each hidden layer node is passed as input to the next layer of nodes. The output layer of nodes receives input from the last hidden layer and generates the result of the neural network. For example, in certain embodiments in which machine learning algorithm 2112 is configured to classify sensor inputs 2122 as either associated with an algorithmic cart 1420 that is a good candidate for verification (e.g., likely includes one or more errors) or an algorithmic cart 1420 that is not a good candidate for verification (e.g., likely does not include one or more errors), the output layer may include a single output node, where an output value of the node between 0.5 and 1.0 indicates that sensor inputs 2122 are associated with an algorithmic cart 1420 that is a good candidate for verification and an output value of the node less than 0.5 indicates that sensor inputs 2122 are associated with an algorithmic cart 1420 that is not a good candidate for verification. Alternatively, the output layer may include two nodes—a first node that is used to categorize sensor inputs 2122 as likely associated with an algorithmic cart 1420 that is a good candidate for verification, and a second node that is used to categorize sensor inputs 2122 as likely associated with an algorithmic cart 1420 that is not a good candidate for verification. Sensor inputs 2122 are assigned to one of these two categories according to which of the two output nodes has the largest value.

In embodiments in which machine learning algorithm 2112 is a neural network, the neural network may be trained to identify those algorithmic shopping carts 1420 that are good candidates for verification (e.g., those algorithmic shopping carts 1420 that likely include one or more errors) in any suitable manner. For example, in certain embodiments, machine learning algorithm 2112 is trained using training data 2106 by applying the algorithm to historical sensor inputs 2118, for which the desired output of the neural network, indicated by labels 2120, is known. Training the algorithm involves optimizing the values of the weights of the nodes within the network in order to maximize the accuracy of the neural network (e.g., maximize the likelihood that, when applied to a given set of historical sensor inputs 2118, the output of the neural network will correspond to the label 2120 assigned to the given set of historical sensor inputs 2118). In certain embodiments, cart review assistant 2102 performs this training process.

As a second example of the type of machine learning algorithm 2112 that may be used by cart review assistant 2102 to identify those algorithmic shopping carts 1420 that are good candidates for verification by virtual store tool 405 (e.g., those algorithmic shopping carts 1420 that likely include one or more errors), in certain embodiments, machine learning algorithm 2112 is a reinforcement learning algorithm, that continues to learn over time. As a specific example, machine learning algorithm 2112 may correspond to a reinforcement learning agent. For each decision made by reinforcement learning agent 2112, feedback 2116 may be provided to the agent, in the form of a reward/penalty, and the agent may use this feedback 2116 to refine itself in an attempt to maximize future rewards. Rewards/penalties may be provided to the agent as follows: (1) if the agent determines that an algorithmic shopping cart 1420 obtained from set of sensor inputs 2122 is a good candidate for verification (e.g., the cart is likely inaccurate) and it is subsequently determined that the algorithmic shopping cart 1420 does include one or more errors, a first reward value is provided to the reinforcement learning agent 2112; (2) if the agent determines that an algorithmic shopping cart 1420 obtained from set of sensor inputs 2122 is a good candidate for verification (e.g., the cart is likely inaccurate) and it is subsequently determined that the algorithmic shopping cart 1420 does not include any errors, a first penalty value (e.g., a first negative reward value) is provided to the reinforcement learning agent 2112; (3) if the agent determines that an algorithmic shopping cart 1420 obtained from set of sensor inputs 2122 is not a good candidate for verification (e.g., the cart is likely accurate) and it is subsequently determined that the algorithmic shopping cart 1420 does not include any errors, a second reward value is provided to the reinforcement learning agent 2112; and (4) if the agent determines that an algorithmic shopping cart 1420 obtained from set of sensor inputs 2122 is a not a good candidate for verification (e.g., the cart is likely accurate) and it is subsequently determined that the algorithmic shopping cart 1420 does not include one or errors, a second penalty value (e.g., a second negative reward value) is provided to the reinforcement learning agent 2112. In certain embodiments, the reward/penalty values are determined using a reward function. In certain embodiments, the first and second reward values are the same and the first and second penalty values are the same. In some embodiments, the absolute value of the second penalty value is larger than the absolute value of the first penalty value (e.g., reinforcement learning agent 2112 is penalized more heavily for determinations that result in incorrect charges to customer 105 than for determinations that result in an unnecessary expenditure of computational resources). In certain embodiments, the values of one or more of the first reward, the second reward, the first penalty, and the second penalty, may change, depending on the situation under consideration by reinforcement learning agent 2112. As an example, a larger reward value may be provided to reinforcement learning agent 2112 when the agent determines that a first algorithmic shopping cart 1420 is a good candidate for verification (e.g., the cart is likely inaccurate) and it is subsequently determined that the cart includes error(s) that would lead to under/overcharging customer 105 by at least twenty dollars, as compared to a situation in which the agent determines that a second algorithmic shopping cart 1420 is a good candidate for verification and it is subsequently determined that the cart includes error(s) that would lead to under/overcharging customer 105 by only a few cents. The process of rewarding/penalizing reinforcement learning agent 2112 is described in further detail below, in the discussion of FIG. 22.

Cart review assistant 2102 may include any suitable reinforcement learning agent 2112. As an example, in certain embodiments, cart review assistant 2102 includes a tabular Q-Learning model that includes a set of state-action pairs, with a q-value assigned to each pair. Each state corresponds to a set of sensor inputs 2122 and each action corresponds to either sending the algorithmic shopping cart 1420 that is associated with the sensor inputs 2122 for verification by virtual store tool 405, or not sending the algorithmic shopping cart 1420 for review. For a given state, the action with the highest q-value is the action that is chosen by reinforcement learning agent 2112. For example, for a given set of sensor inputs 2122, reinforcement learning agent 2112 makes the decision of whether or not to send the associated algorithmic shopping cart 1420 for review by virtual store tool 405 based on which action (sending the cart for review or not sending the cart for review) corresponds to the highest q-value. Feedback 2116 may be provided to agent 2112 in the form of the reward/penalty values described above. In response to receiving this feedback 2116, agent 2112 may refine the Q-learning model by adjusting the q-values assigned to the state-action pairs in order to maximize the future rewards received (e.g., to maximize the Bellman equation). In certain embodiments, sensor inputs 2122 are discretized, such that a finite and computationally feasible number of states are possible. For example, in embodiments in which inputs 2122 include the positions of customer 105 within physical store 100, those positions may be specified according to grid points on a discrete grid overlaying the floor of physical store 100.

As another example of a reinforcement learning agent 2112 that may be used by cart review assistant 2102, in certain embodiments, reinforcement learning agent 2112 is a deep Q network (DQN). For example, reinforcement learning agent 2112 may be a deep Q network that includes an input layer of nodes, one or more hidden layers of nodes, and an output layer of nodes. The input layer of nodes may be configured to provide, as input to the network, the set of sensor inputs 2122. The output layer of nodes may be configured to provide, as output, q-values corresponding to the actions of sending the algorithmic shopping cart 1420 associated with the sensor inputs 2122 for review by virtual store tool 405 or not sensing algorithmic shopping cart 1420 for review by virtual store tool 405. Feedback 2116 may be provided to the agent 2112 in the form of the reward/penalty values described above. In response to receiving this feedback 2116, the agent may refine itself (e.g., by adjusting the values of weights within the network) in order to maximize the future rewards it receives (e.g., to maximize the Bellman equation). In certain embodiments, reinforcement learning agent 2112 is a variant of a deep Q network. For example, in some embodiments, machine learning agent 2112 corresponds to a double deep Q network (DDQN). The use of a DDQN algorithm may be desirable to reduce the impact of recency bias on the algorithm's determinations.

In certain embodiments, system 2100 includes database 2104. Database 2104 stores training data 2106. Training data 2106 includes any data that may be used to train machine learning algorithm 2112. For example, in embodiments in which machine learning algorithm 2112 is a supervised learning algorithm, training data 2106 may include sets of historical sensor inputs 2118 a through 2118 n and labels 2120 a through 2120 n.

Each set of historical sensor inputs 2118 includes inputs received from sensors 498 (including, for example, cameras 1305 and weight sensors 1300) located within physical store 100 that were captured during a shopping session of a customer 105 within physical store 100. Sensors 498 may include any sensors located within physical store 100. For example, in certain embodiments, sensors 498 may include cameras, light detection and range sensors, millimeter wave sensors, weight sensors, and/or any other appropriate sensors, operable to track a customer 105 in physical store 100 and to detect information associated with customer 105 selecting one or more physical items 315 from physical store 100.

Each label 2120 of labels 2120 a through 2120 n is assigned to a corresponding set of historical sensor inputs 2118 a through 2118 n, and labels whether the algorithmic shopping cart 1420, generated by algorithm 488 based on the corresponding set of historical sensor inputs 2118, included any errors (and, accordingly, whether algorithmic shopping cart 1420 is a good candidate for verification by virtual store tool 405). As an example, label 2120 a, assigned to set of historical sensor inputs 2118 a, may indicate that an algorithmic shopping cart 1420, generated by algorithm 488 based on set of historical sensor inputs 2118 a, included one or more errors. As another example, label 2120 b, assigned to set of historical sensor inputs 2118 b, may indicate that an algorithmic shopping cart 1420, generated by algorithm 488 based on set of historical sensor inputs 2118 b, did not include any errors.

In certain embodiments, in addition to labeling sets of historical sensor inputs 2118 according to whether an algorithmic shopping cart 1420, generated by algorithm 488 based on the corresponding set of historical sensor inputs 2118, included any errors, labels 2120 may further label sets of historical sensor inputs 2118 according to the probable type and/or cause of the error(s) likely present in the algorithmic shopping cart 1420. As an example, label 2120 a may label set of historical sensor inputs 2118 a according to whether an algorithmic shopping cart 1420, generated by algorithm 488 based on the set of historical sensor inputs 2118 a : (1) included one or more physical items 315 that customer 105 did not select; (2) did not include one or more physical items 315 that customer 105 did select; (3) included an incorrect quantity of one or more physical items 315 that customer 105 selected; and/or (4) included any other type of error. As another example, label 2120 a may label set of historical sensor inputs 2118 a according to whether an algorithmic shopping cart 1420, generated by algorithm 488 based on the set of historical sensor inputs 2118 included one or more errors because: (1) one or more servers within external system 485 likely was not functioning properly; (2) external system 485 likely was not properly recording information received from one or more weight sensors 1300; (3) one or more weight sensors 1300 likely generated a false positive; (4) algorithm 488 likely generated one or more incorrect item counts; (5) algorithm 488 likely assigned one or more items 315 to the wrong customer 105; (6) algorithm 488 likely lost track of customer 105 and/or (7) any other likely cause of the error(s) within algorithmic shopping cart 1420.

Labels 2120 may be generated in any suitable manner. As an example, in certain embodiments, virtual store tool 405 may generate a label 2120 a, corresponding to historical sensor inputs 2118 a that were generated during a given shopping session in physical store 100, in response to generating a virtual shopping cart 420 for that shopping session and comparing the virtual shopping cart 420 to an algorithmic shopping cart 1420, generated by algorithm 488 based on the historical sensor inputs 2118 a. For example, for a given shopping session associated with sensor inputs 2118 a, in response to comparing virtual shopping cart 420 with algorithmic shopping cart 1420 and determining that the two carts do not match, virtual store tool 405 may generate label 2120 a, which indicates that the algorithmic shopping cart 1420 associated with sensor inputs 2118 a is a good candidate for verification. Similarly, for a given shopping session associated with sensor inputs 2118 b, in response to comparing virtual shopping cart 420 with algorithmic shopping cart 1420 and determining that the two carts match, virtual store tool 405 may generate label 2120 b, which indicates that the algorithmic shopping cart 1420 associated with sensor inputs 2118 b is not a good candidate for verification. As another example, in certain embodiments, a user 120 may generate labels 2120. For example, user 120 may generate label 2120 c based on an analysis of historical sensor inputs 2118c.

Modifications, additions, or omissions may be made to system 2100 without departing from the scope of the invention. For example, system 2100 may include any number of users 120, virtual store tools 405, networks 430, external systems 485, layout cameras 490, rack cameras 495, sensors 498, and databases 2104. The components may be integrated or separated. Moreover, the operations may be performed by more, fewer, or other components. Additionally, the operations may be performed using any suitable logic comprising software, hardware, and/or other logic.

b. Operation of the Cart Review Assistant

FIG. 22 illustrates an example process by which machine learning algorithm 2112 of cart review assistant 2102 (1) determines whether or not virtual store tool 405 should be used to verify an algorithmic shopping cart 1420 (e.g., whether or not algorithmic shopping cart 1420 is likely accurate) and (2) receives feedback 2116 for its decision. As illustrated in FIG. 22, machine learning algorithm 2112 operates on sensor inputs 2122.

Sensor inputs 2122 include inputs received from sensors 498 located within physical store 100 and were used by algorithm 488 to generate algorithmic shopping cart 1420. Sensors 498 may include any sensors located within physical store 100. For example, in certain embodiments, sensors 498 may include cameras, light detection and range sensors, millimeter wave sensors, weight sensors, and/or any other appropriate sensors, operable to track a customer 105 in physical store 100 and to detect information associated with customer 105 selecting one or more physical items 315 from physical store 100.

In response to receiving sensor inputs 2122, machine learning algorithm 2112 uses these inputs to determine whether or not the algorithmic shopping cart 1420, obtained by algorithm 488 using sensor inputs 2122, is a good candidate for verification by virtual store tool 405. As described above, determining whether or not algorithmic shopping cart 1420 is a good candidate for verification may include determining whether or not a probability that algorithmic shopping cart 1420 includes one or more errors is greater than a threshold.

i. Decision to Send an Algorithmic Shopping Cart to Virtual Store Tool for Review.

In response to determining that algorithmic shopping cart 1420 is a good candidate for verification, cart review assistant 2102 sends the shopping session associated with the algorithmic shopping cart 1420 to virtual store tool 405 for review. Virtual store tool 405 is then used to virtually emulate the shopping session associated with algorithmic shopping cart 1420, as described in Section IV above. In response to virtually emulating the shopping session, virtual store tool 405 compares the virtual shopping cart 420 that was generated during the virtual emulation to algorithmic shopping cart 1420, as described in Section VI. Virtual store tool 405 then provides feedback 2116 to cart review assistant 2102, which indicates whether or not virtual shopping cart 1420 matched algorithmic shopping cart 420. In certain embodiments, virtual store tool 405 additionally charges an account belonging to customer 105 for the purchase price of the items 320 in virtual shopping cart 420 and transmits a receipt 1465 for the purchase to customer 105.

Cart review assistant 2102 may use the feedback 2116 received from virtual store tool 405 in any suitable manner. As an example, in certain embodiments in which machine learning algorithm 2112 is a reinforcement learning agent, in response to receiving feedback 2116 from virtual store tool 405, cart review assistant 2102 uses this feedback to reward/punish agent 2112. For instance, in response to receiving feedback 2116 indicating that virtual shopping cart 1420 and algorithmic shopping cart 420 did not match, cart review assistant 2102 provides reinforcement learning agent 2112 with a positive reward value, rewarding reinforcement learning agent 2112 for deciding to send the shopping session associated with algorithmic shopping cart 1420 to virtual store tool 405 for review, given that virtual store tool 405 determined that algorithmic shopping cart 1420 included at least one error. In a similar manner, in response to receiving feedback 2116 indicating that virtual shopping cart 1420 matched algorithmic shopping cart 420, cart review assistant 2102 provides reinforcement learning agent 2112 with a penalty value (e.g., a negative reward value), penalizing reinforcement learning agent 2112 for deciding to send the shopping session associated with algorithmic shopping cart 1420 to virtual store tool 405 for review (and therefore causing the system to expend additional processing resources), given that virtual store tool 405 determined that algorithm shopping cart 1420 did not include any errors.

As another example of the use of feedback 2116 received from virtual store tool 405, in certain embodiments in which machine learning algorithm 2112 is a supervised learning algorithm, in response to receiving feedback 2116 from virtual store tool 405, cart review assistant 2102 may use this feedback to generate a label 2120 for sensor inputs 2122. Label 2120 labels whether the algorithmic shopping cart 1420, generated by algorithm 488 based on sensor inputs 2122, included any errors (and, accordingly, whether algorithmic shopping cart 1420 was a good candidate for verification by virtual store tool 405). Cart review assistant 2102 may then store label 2120 and sensor inputs 2122 in database 2104, thereby adding to training data 2106. As an example, in response to receiving feedback 2116 indicating that virtual shopping cart 420 did not match algorithmic shopping cart 1420, cart review assistant 2102 may generate a label 2120 for sensor inputs 2122 indicating that the algorithmic shopping cart 1420, generated by algorithm 488 based on sensor inputs 2122, included at least one error (and, accordingly is a good candidate for verification by virtual store tool 405). As another example, in response to receiving feedback 2116 indicating that virtual shopping cart 420 matched algorithmic shopping cart 1420, cart review assistant 2102 may generate a label 2120 for sensor inputs 2122 indicating that the algorithmic shopping cart 1420, generated by algorithm 488 based on sensor inputs 2122, did not include any errors (and, accordingly is not a good candidate for verification by virtual store tool 405). In certain embodiments, cart review assistant 2102 may use training data 2106 to retrain machine learning algorithm 2112. For example, in some embodiments, cart review assistant 2102 may use training data 2106 to retrain machine learning algorithm 2112 at regular intervals.

ii. Decision Not to Send an Algorithmic Shopping Cart to Virtual Store Tool for Review.

In response to determining that algorithmic shopping cart 1420 is not a good candidate for verification, in certain embodiments, cart review assistant 2102 charges an account belonging to customer 105 for the purchase price of the items 320 in algorithmic shopping cart 1420 and transmits a receipt 1465 to customer 105. In some embodiments, cart review assistant 2102 instructs another component of system 2100, such as virtual store tool 405, external system 485, or any other suitable component, to perform such tasks.

Feedback 2116 for a decision by machine learning algorithm 2112 not to send the shopping session associated with algorithmic shopping cart 1420 to virtual store tool 405 may be generated in any suitable manner. As an example, in certain embodiments, the feedback 2116 for a decision by machine learning algorithm 2112 not to send the shopping session associated with algorithmic shopping cart 1420 to virtual store tool 405 for review may originate from customer 105, in the form of customer feedback 2208. For example, in certain embodiments, in response to receiving receipt 1465, customer 105 may submit a refund request 2208 to virtual store tool 405, disputing one or more charges, as described in Section VII above. In response to receiving the refund request, virtual store tool 405 may determine whether or not to issue a refund to an account belonging to customer 105, as described in detail in Section VII above. If virtual store tool 405 determines to issue a refund to customer 105, this may indicate that the algorithmic shopping cart 1420 that was used to charge the account belonging to customer 105, included one or more errors. Accordingly, virtual store tool 405 may provide this information to cart review assistant 2102 as feedback 2116. On the other hand, if virtual store tool 405 determines not to issue a refund to customer 105, this may indicate that the algorithmic shopping cart 1420, used to charge the account belonging to customer 105, did not include any errors. Accordingly, virtual store tool 405 may provide this information to cart review assistant 2102 as feedback 2116.

As another example of the manner by which feedback 2116 may be generated, in certain embodiments, the feedback 2116 for a decision by machine learning algorithm 2112 not to send the shopping session associated with algorithmic shopping cart 1420 to virtual store tool 405 for review may originate directly from virtual store tool 405. For example, in certain embodiments, a customer 105 may be unlikely to submit a refund request 2208 when an error in algorithmic shopping cart 1420 results in system 2100 undercharging customer 105 for his/her shopping session. Accordingly, in order to obtain feedback 2116 related to such shopping sessions, in certain embodiments, cart review assistant 2102 may randomly decide to send a shopping session associated with an algorithmic shopping cart 1420 to virtual store tool 405 for review, even though machine learning algorithm 2112 determined that the shopping session was not a good candidate for review. Virtual store tool 405 may then be used to virtually emulate the shopping session associated with algorithmic shopping cart 1420, as described in Section IV above. In response to virtually emulating the shopping session, virtual store tool 405 compares the virtual shopping cart 420 that was generated during the virtual emulation to algorithmic shopping cart 1420, as described in Section VI. Virtual store tool 405 then provides feedback 2116 to cart review assistant 2102, indicating whether or not virtual shopping cart 1420 matched algorithmic shopping cart 420. In certain embodiments, virtual store tool 405 additionally charges an account belonging to customer 105 for the purchase price of the items 320 in virtual shopping cart 420 and transmits a receipt 1465 for the purchase to customer 105.

Cart review assistant 2102 may use the feedback 2116 received from customer 105 and/or virtual store tool 405 in any suitable manner. As an example, in certain embodiments in which machine learning algorithm 2112 is a reinforcement learning agent, in response to receiving feedback 2116 from virtual store tool 405, cart review assistant 2102 uses this feedback to reward/punish agent 2112. As an example, in response to receiving feedback 2116 indicating that virtual shopping cart 1420 and algorithmic shopping cart 420 did not match, cart review assistant 2102 provides reinforcement learning agent 2112 with a punishment value (e.g., a negative reward value), punishing reinforcement learning agent 2112 for not deciding to send the shopping session associated with algorithmic shopping cart 1420 to virtual store tool 405 for review (and therefore relying on algorithmic shopping cart 1420 to generate receipt 1465), given that virtual store tool 405 determined that algorithmic shopping cart 1420 included at least one error. As another example, in response to receiving feedback 2116 indicating that virtual shopping cart 1420 matched algorithmic shopping cart 420, cart review assistant 2102 provides reinforcement learning agent 2112 with a reward value, rewarding reinforcement learning agent 2112 for deciding not to send the shopping session associated with algorithmic shopping cart 1420 to virtual store tool 405 for review (and therefore conserving processing resources that would otherwise have been expended), given that virtual store tool 405 determined that algorithm shopping cart 1420 did not include any errors.

As another example of the use of feedback 2116 received from customer 105 and/or virtual store tool 405, in certain embodiments in which machine learning algorithm 2112 is a supervised learning algorithm, in response to receiving feedback 2116 from virtual store tool 405, cart review assistant 2102 may use this feedback to generate a label 2120 for sensor inputs 2122. As an example, in response to receiving feedback 2116 indicating that virtual shopping cart 420 did not match algorithmic shopping cart 1420, cart review assistant 2102 may generate a label 2120 for sensor inputs 2122 indicating that the algorithmic shopping cart 1420, generated by algorithm 488 based on sensor inputs 2122, included at least one error (and, accordingly is a good candidate for verification by virtual store tool 405). As another example, in response to receiving feedback 2116 indicating that virtual shopping cart 420 matched algorithmic shopping cart 1420, cart review assistant 2102 may generate a label 2120 for sensor inputs 2122 indicating that the algorithmic shopping cart 1420, generated by algorithm 488 based on sensor inputs 2122, did not include any errors (and, accordingly is not a good candidate for verification by virtual store tool 405). As described above, in certain embodiments, cart review assistant 2102 may use training data 2106 to retrain machine learning algorithm 2112.

c. Method for Selectively Validating Algorithmic Shopping Carts

FIG. 23 presents a flowchart (described in conjunction with elements of FIGS. 21 and 22) illustrating the manner by which cart review assistant 2102 uses machine learning algorithm 2112 to determine whether or not virtual store tool 405 should be used to verify an algorithmic shopping cart 1420 (e.g., whether or not algorithmic shopping cart 1420 is likely accurate) and receives feedback 2116 for this decision. In step 2302 cart review assistant 2102 receives sensor inputs 2122 associated with a shopping session of a customer 105 in physical store 100. In certain embodiments, sensor inputs 2122 were used by algorithm 488 to generate an algorithmic shopping cart 1420 for the shopping session of customer 105. In step 2304 cart review assistant 2102 applies machine learning algorithm 2112 to sensor inputs 2122 to determine whether or not algorithmic shopping cart 1420 is a good candidate for review.

In step 2306 cart review assistant 2102 determines whether machine learning algorithm 2112 determined that algorithmic shopping cart 1420 is a good candidate for review (e.g., whether algorithmic shopping cart 1420 likely includes one or more errors). If, in step 2306 cart review assistant 2102 determines that machine learning algorithm 2112 determined that algorithmic shopping cart 1420 is a good candidate for review, in step 2308 cart review assistant 2102 instructs virtual store tool 405 to virtually emulate the shopping session of customer 105 that is associated with sensor inputs 2122. This virtual emulation may include (1) generating a virtual shopping cart 420 associated with the shopping session, (2) using virtual shopping cart 420 to charge an account belonging to customer 105 for the selections he/she made during the shopping session, and (3) issuing a receipt 1465 to customer 105. In step 2310 virtual store tool 405 determines whether the virtual shopping cart 420 matches the algorithmic shopping cart 1420. If, in step 2310 virtual store tool 405 determines that virtual shopping cart 420 does not match algorithmic shopping cart 1420, in step 2312 virtual store tool 405 provides feedback 2116 to cart review assistant 2102, indicating that virtual shopping cart 420 does not match algorithmic shopping cart 1420. In certain embodiments in which machine learning algorithm 2112 is a reinforcement learning agent, cart review assistant 2102 then uses this feedback to provide a reward to reinforcement learning agent 2112. Method 2300 then proceeds to step 2326. If, in step 2310 virtual store tool 405 determines that virtual shopping cart 420 matches algorithmic shopping cart 1420, in step 2314 virtual store tool 405 provides feedback 2116 to cart review assistant 2102, indicating that virtual shopping cart 420 matches algorithmic shopping cart 1420. In certain embodiments in which machine learning algorithm 2112 is a reinforcement learning agent, cart review assistant 2102 then uses this feedback to provide a penalty to reinforcement learning agent 2112. Method 2300 then proceeds to step 2326.

If, in step 2306 cart review assistant 2102 determines that machine learning algorithm 2112 determined that algorithmic shopping cart 1420 is not a good candidate for review, in step 2316 cart review assistant 2102 uses algorithmic shopping cart 1420 to charge an account belonging to customer 105 for the selections he/she made during the shopping session and issue a corresponding receipt 1465 to customer 105. In step 2318 cart review assistant 2102 receives feedback 2208 from customer 105. In certain embodiments, feedback 2208 is a refund request submitted by customer 105 to virtual store tool 405. Virtual store tool 405 then processes the refund request by virtually emulating the shopping session associated with the refund request. In step 2320 virtual store tool 405 determines whether the virtual cart 420 resulting from the virtual emulation of the shopping session associated with the refund request matches the algorithmic shopping cart 1420 that was used to charge customer 105. If, in step 2320 virtual store tool 405 determines that virtual shopping cart 420 does not match algorithmic shopping cart 1420, in step 2322 virtual store tool 405 provides feedback 2116 to cart review assistant 2102, indicating that virtual shopping cart 420 does not match algorithmic shopping cart 1420. In certain embodiments in which machine learning algorithm 2112 is a reinforcement learning agent 2112, cart review assistant 2102 then uses this feedback to provide a penalty to reinforcement learning agent 2112. Method 2300 then proceeds to step 2326. If, in step 2320 virtual store tool 405 determines that virtual shopping cart 420 matches algorithmic shopping cart 1420, in step 2324 virtual store tool 405 provides feedback 2116 to cart review assistant 2102, indicating that virtual shopping cart 420 matches algorithmic shopping cart 1420. In certain embodiments in which machine learning algorithm 2112 is a reinforcement learning agent, cart review assistant 2102 then uses this feedback to provide a reward to reinforcement learning agent 2112. Method 2300 then proceeds to step 2326. In step 2326 cart review assistant 2102 uses the punishment and reward values to refine machine learning algorithm, 2112.

In certain embodiments, feedback 2208 received by cart review assistant 2102 at step 2318 includes the absence of a refund request submitted by customer 105 to virtual store tool 405. For example, in certain embodiments, if virtual store tool 405 does not receive a refund request from customer 105 within a set period of time, cart review assistant 2102 may assume that customer 105 determined that algorithmic shopping cart 1402 was accurate. Accordingly, in certain embodiments, at step 2320 cart review assistant 2102 may assume that if virtual store tool 405 were to virtually emulate the corresponding shopping session, the resulting virtual shopping cart 420 would match algorithmic shopping cart 1420. In some embodiments, even though virtual store tool 405 did not receive a refund request from customer 105 within a set period of time, cart review assistant 2102 may nevertheless randomly decide to instruct virtual store tool 405 to virtually emulate the shopping session of customer 105, and to compare the resulting virtual shopping cart 420 to algorithmic shopping cart 1420 at step 2320. This may be desirable in certain embodiments where errors in algorithmic shopping cart 1420 may result in customer 105 being undercharged.

Modifications, additions, or omissions may be made to method 2300 depicted in FIG. 23. Method 2300 may include more, fewer, or other steps. For example, steps may be performed in parallel or in any suitable order. While discussed as cart review assistant 2102 (or components thereof) and virtual store tool 405 (or components thereof) performing the steps, any suitable component of system 2100, such as external system 485 for example, may perform one or more steps of the method.

While the preceding examples and explanations are described with respect to particular use cases within a retail environment, one of ordinary skill in the art would readily appreciate that the previously described configurations and techniques may also be applied to other applications and environments. Examples of other applications and environments include, but are not limited to, security applications, surveillance applications, object tracking applications, people tracking applications, occupancy detection applications, logistics applications, warehouse management applications, operations research applications, product loading applications, retail applications, robotics applications, computer vision applications, manufacturing applications, safety applications, quality control applications, food distributing applications, retail product tracking applications, mapping applications, simultaneous localization and mapping (SLAM) applications, 3D scanning applications, autonomous vehicle applications, virtual reality applications, augmented reality applications, or any other suitable type of application.

Although the present disclosure includes several embodiments, a myriad of changes, variations, alterations, transformations, and modifications may be suggested to one skilled in the art, and it is intended that the present disclosure encompass such changes, variations, alterations, transformations, and modifications as falling within the scope of the appended claims. 

What is claimed is:
 1. An apparatus comprising: a memory configured to store: a first set of inputs comprising information collected from sensors located in a physical store during a shopping session of a first person in the physical store; a first algorithmic shopping cart comprising a first set of items, the first set of items determined by an algorithm, based on the first set of inputs, to have been selected by the first person during the shopping session of the first person; and instructions corresponding to a machine learning algorithm configured, when implemented by a hardware processor, to use the first set of inputs to select between using the first algorithmic shopping cart to process a first transaction and using a first virtual shopping cart to process the first transaction, wherein: the first transaction is associated with the shopping session of the first person; and the first virtual shopping cart comprises items associated with the shopping session of the first person; the hardware processor communicatively coupled to the memory, the hardware processor configured to: use the machine learning algorithm to determine, based on the first set of inputs, to use the first algorithmic shopping cart to process the first transaction, wherein the first set of inputs are associated with a first probability that the first algorithmic shopping cart is accurate, the first probability greater than a threshold; and in response to determining to use the first algorithmic shopping cart to process the first transaction: generate a first receipt based on the first algorithmic shopping cart, the first receipt comprising a first set of prices, each price of the first set of prices corresponding to an item of the first set of items; and send the first receipt to the first person.
 2. The apparatus of claim 1, wherein: the memory is further configured to store: a second set of inputs comprising information collected from the sensors during a shopping session of a second person in the physical store; a second virtual shopping cart comprising a second set of items associated with the shopping session of the second person, wherein video of the shopping session of the second person was captured by a set of cameras located in the physical store, the video depicting the second person selecting the second set of items; and a second algorithmic shopping cart comprising a third set of items, the third set of items determined by the algorithm, based on the second set of inputs, to have been selected by the second person during the shopping session of the second person; the instructions corresponding to the machine learning algorithm are further configured, when implemented by the hardware processor, to use the second set of inputs to select between using the second algorithmic shopping cart to process a second transaction and using the second virtual shopping cart to process the second transaction, wherein the second transaction is associated with the shopping session of the second person; and the hardware processor is further configured to: use the machine learning algorithm to determine, based on the second set of inputs, to use the second virtual shopping cart to process the second transaction associated with the shopping session of the second person, wherein, the second set of inputs are associated with a second probability that the second algorithmic shopping cart is accurate, the second probability less than the threshold; and in response to determining to use the second virtual shopping cart to process the second transaction: generate a second receipt based on the second virtual shopping cart, the second receipt comprising a second set of prices, each price of the second set of prices corresponding to an item of the second set of items; and send the second receipt to the second person.
 3. The apparatus of claim 1, wherein: the memory is further configured to store: a third set of inputs comprising information collected from the sensors during a shopping session of a third person in the physical store; a third algorithmic shopping cart comprising a third set of items, the third set of items determined by the algorithm, based on the third set of inputs, to have been selected by the third person during the shopping session of the third person, the third set of items comprising: a first subset of items associated with a first portion of the shopping session of the third person, the first subset of items comprising a first item; and a second subset of items associated with a second portion of the shopping session of the third person; and a third virtual cart comprising a second item associated with the second portion of the shopping session of the third person, wherein video of the shopping session of the third person was captured by the set of cameras located in the physical store, the video depicting the third person selecting the second item; and the instructions corresponding to the machine learning algorithm are further configured, when implemented by the hardware processor, to use the third set of inputs to: select between using the third algorithmic shopping cart to process a first portion of a third transaction and using a third virtual cart to process the first portion of the third transaction, the first portion of the third transaction associated with the first portion of the shopping session; and select between using the third algorithmic shopping cart to process a second portion of the third transaction and using the third virtual cart to process the second portion of the third transaction, the second portion of the third transaction associated with the second portion of the shopping session; and the hardware processor is further configured to: use the machine learning algorithm to determine, based on the third set of inputs, to use the third algorithmic shopping cart to process the first portion of the third transaction associated with the shopping session of the third person and to use the third virtual shopping cart to process the second portion of the third transaction, wherein: the third set of inputs are associated with a third probability that the first subset of items of the third algorithmic shopping cart is accurate, the third probability greater than the threshold; and the third set of inputs are associated with a fourth probability that the second subset of items of the third algorithmic shopping cart is accurate, the fourth probability less than the threshold; in response to determining to use the third algorithmic shopping cart to process the first portion of the third transaction and to use the third virtual shopping cart to process the second portion of the third transaction: generate a third receipt comprising: a first price corresponding to the first item; and a second price corresponding to the second item; and send the third receipt to the third person.
 4. The apparatus of claim 1, wherein: the machine learning algorithm is a supervised learning algorithm; and the hardware processor is further configured to train the machine learning algorithm, wherein training the machine learning algorithm comprises providing the machine learning algorithm with a collection of historical sets of inputs and a set of labels, wherein: each historical set of inputs of the collection of historical sets of inputs comprises information collected from the sensors during a historical shopping session in the physical store; and each label of the set of labels corresponds to a historical set of inputs of the collection of historical sets of inputs and labels a historical algorithmic shopping cart, determined by the algorithm, based on the historical set of inputs, as either accurate or inaccurate.
 5. The apparatus of claim 4, wherein: a first label, labelling a first historical algorithmic shopping cart as accurate, indicates that the first historical algorithmic shopping cart matches a first historical virtual shopping cart, wherein: the first historical algorithmic shopping cart comprises a first historical set of items, the first historical set of items determined by the algorithm, based on a first historical set of inputs comprising information collected from the sensors during a first historical shopping session in the physical store, to have been selected during the first historical shopping session; the first historical virtual shopping cart comprises a second historical set of items associated with the first historical shopping session, wherein video of the first historical shopping session was captured by the set of cameras located in the physical store, the video of the first historical shopping session depicting the second historical set of items being selected; and the first historical set of items matches the second historical set of items; and a second label, labelling a second historical algorithmic shopping cart as inaccurate, indicates that the second historical algorithmic shopping cart does not match a second historical virtual shopping cart, wherein: the second historical algorithmic shopping cart comprises a third historical set of items, the third historical set of items determined by the algorithm, based on a second historical set of inputs comprising information collected from the sensors during a second historical shopping session in the physical store, to have been selected during the second historical shopping session; and the second historical virtual shopping cart comprises a fourth historical set of items associated with the second historical shopping session, wherein video of the second historical shopping session was captured by the set of cameras located in the physical store, the video of the second historical shopping session depicting the fourth historical set of items being selected; and the third historical set of items does not match the fourth historical set of items.
 6. The apparatus of claim 1, wherein the machine learning algorithm is a reinforcement learning algorithm.
 7. The apparatus of claim 6, wherein the reinforcement learning algorithm is a double deep-Q network.
 8. A method comprising: using a machine learning algorithm to determine, based on a first set of inputs, to use a first algorithmic shopping cart to process a first transaction, wherein: the machine learning algorithm is configured to use the first set of inputs to select between using the first algorithmic shopping cart to process the first transaction and using a first virtual shopping cart to process the first transaction, the first virtual shopping cart comprising items associated with the shopping session of the first person; the first algorithmic shopping cart comprises a first set of items, the first set of items determined by an algorithm, based on the first set of inputs, to have been selected by the first person during the shopping session of the first person; the first set of inputs comprises information collected from sensors located in a physical store during the shopping session of the first person in the physical store and is associated with a first probability that the first algorithmic shopping cart is accurate, the first probability greater than a threshold; and the first transaction is associated with the shopping session of the first person; and in response to determining to use the first algorithmic shopping cart to process the first transaction: generating a first receipt based on the first algorithmic shopping cart, the first receipt comprising a first set of prices, each price of the first set of prices corresponding to an item of the first set of items; and sending the first receipt to the first person.
 9. The method of claim 8, further comprising: using the machine learning algorithm to determine, based on a second set of inputs, to use a second virtual shopping cart to process a second transaction associated with a shopping session of a second person, wherein: the second virtual shopping cart comprises a second set of items associated with the shopping session of the second person, wherein video of the shopping session of the second person was captured by a set of cameras located in the physical store, the video depicting the second person selecting the second set of items; the machine learning algorithm is further configured to use the second set of inputs to select between using a second algorithmic shopping cart to process the second transaction and using the second virtual shopping cart to process the second transaction, the second algorithmic shopping cart comprising a third set of items, the third set of items determined by the algorithm, based on the second set of inputs, to have been selected by the second person during the shopping session of the second person; the second set of inputs comprise information collected from the sensors during the shopping session of the second person in the physical store and is associated with a second probability that the second algorithmic shopping cart is accurate, the second probability less than the threshold; and in response to determining to use the second virtual shopping cart to process the second transaction: generating a second receipt based on the second virtual shopping cart, the second receipt comprising a second set of prices, each price of the second set of prices corresponding to an item of the second set of items; and sending the second receipt to the second person.
 10. The method of claim 8, wherein: using the machine learning algorithm to determine, based on a third set of inputs, to use a third algorithmic shopping cart to process a first portion of a third transaction associated with a shopping session of a third person and to use a third virtual shopping cart to process a second portion of the third transaction, wherein: the machine learning algorithm is further configured to use the third set of inputs to: select between using the third algorithmic shopping cart to process the first portion of the third transaction and using the third virtual cart to process the first portion of the third transaction, the first portion of the third transaction associated with a first portion of the shopping session; and select between using the third algorithmic shopping cart to process the second portion of the third transaction and using the third virtual cart to process the second portion of the third transaction, the second portion of the third transaction associated with a second portion of the shopping session; the third algorithmic shopping cart comprises a third set of items, the third set of items determined by the algorithm, based on the third set of inputs, to have been selected by the third person during the shopping session of the third person, the third set of items comprising: a first subset of items associated with the first portion of the shopping session of the third person, the first subset of items comprising a first item; and a second subset of items associated with the second portion of the shopping session of the third person; the third virtual cart comprises a second item associated with the second portion of the shopping session of the third person, wherein video of the shopping session of the third person was captured by the set of cameras located in the physical store, the video depicting the third person selecting the second item; and the third set of inputs comprises information collected from the sensors during the shopping session of the third person in the physical store and is associated with: a third probability that the first subset of items of the third algorithmic shopping cart is accurate, the third probability greater than the threshold; and a fourth probability that the second subset of items of the third algorithmic shopping cart is accurate, the fourth probability less than the threshold; in response to determining to use the third algorithmic shopping cart to process the first portion of the third transaction and to use the third virtual shopping cart to process the second portion of the third transaction: generating a third receipt comprising: a first price corresponding to the first item; and a second price corresponding to the second item; and sending the third receipt to the third person.
 11. The method of claim 8, wherein: the machine learning algorithm is a supervised learning algorithm; and the method further comprises training the machine learning algorithm, wherein training the machine learning algorithm comprises providing the machine learning algorithm with a collection of historical sets of inputs and a set of labels, wherein: each historical set of inputs of the collection of historical sets of inputs comprises information collected from the sensors during a historical shopping session in the physical store; and each label of the set of labels corresponds to a historical set of inputs of the collection of historical sets of inputs and labels a historical algorithmic shopping cart, determined by the algorithm, based on the historical set of inputs, as either accurate or inaccurate.
 12. The method of claim 11, wherein: a first label, labelling a first historical algorithmic shopping cart as accurate, indicates that the first historical algorithmic shopping cart matches a first historical virtual shopping cart, wherein: the first historical algorithmic shopping cart comprises a first historical set of items, the first historical set of items determined by the algorithm, based on a first historical set of inputs comprising information collected from the sensors during a first historical shopping session in the physical store, to have been selected during the first historical shopping session; the first historical virtual shopping cart comprises a second historical set of items associated with the first historical shopping session, wherein video of the first historical shopping session was captured by the set of cameras located in the physical store, the video of the first historical shopping session depicting the second historical set of items being selected; and the first historical set of items matches the second historical set of items; and a second label, labelling a second historical algorithmic shopping cart as inaccurate, indicates that the second historical algorithmic shopping cart does not match a second historical virtual shopping cart, wherein: the second historical algorithmic shopping cart comprises a third historical set of items, the third historical set of items determined by the algorithm, based on a second historical set of inputs comprising information collected from the sensors during a second historical shopping session in the physical store, to have been selected during the second historical shopping session; and the second historical virtual shopping cart comprises a fourth historical set of items associated with the second historical shopping session, wherein video of the second historical shopping session was captured by the set of cameras located in the physical store, the video of the second historical shopping session depicting the fourth historical set of items being selected; and the third historical set of items does not match the fourth historical set of items.
 13. The method of claim 8, wherein the machine learning algorithm is a reinforcement learning algorithm.
 14. The method of claim 13, wherein the reinforcement learning algorithm is a double deep-Q network.
 15. An apparatus comprising: a memory configured to store: a first set of inputs comprising information collected from sensors located in a physical store during a shopping session of a first person in the physical store; a first algorithmic shopping cart comprising a first set of items, the first set of items determined by an algorithm, based on the first set of inputs, to have been selected by the first person during the shopping session of the first person; a first virtual shopping cart comprising a second set of items associated with the shopping session of the first person, wherein video of the shopping session of the first person was captured by a set of cameras located in the physical store, the video depicting the first person selecting the second set of items; and instructions corresponding to a machine learning algorithm configured, when implemented by a hardware processor, to use the first set of inputs to select between using the first algorithmic shopping cart to process a first transaction and using the first virtual shopping cart to process the first transaction, wherein the first transaction is associated with the shopping session of the first person; and the hardware processor communicatively coupled to the memory, the hardware processor configured to: use the machine learning algorithm to determine, based on the first set of inputs, to use the first virtual shopping cart to process the first transaction, wherein the first set of inputs are associated with a first probability that the first algorithmic shopping cart is accurate, the first probability less than a threshold; and in response to determining to use the first virtual shopping cart to process the first transaction: generate a first receipt based on the first virtual shopping cart, the first receipt comprising a first set of prices, each price of the first set of prices corresponding to an item of the second set of items; and send the first receipt to the first person.
 16. The apparatus of claim 15, wherein: the machine learning algorithm is a supervised learning algorithm; and the hardware processor is further configured to train the machine learning algorithm, wherein training the machine learning algorithm comprises providing the machine learning algorithm with a collection of historical sets of inputs and a set of labels, wherein: each historical set of inputs of the collection of historical sets of inputs comprises information collected from the sensors during a historical shopping session in the physical store; and each label of the set of labels corresponds to a historical set of inputs of the collection of historical sets of inputs and labels a historical algorithmic shopping cart, determined by the algorithm, based on the historical set of inputs, as either accurate or inaccurate.
 17. The apparatus of claim 16, wherein: a first label, labelling a first historical algorithmic shopping cart as accurate, indicates that the first historical algorithmic shopping cart matches a first historical virtual shopping cart, wherein: the first historical algorithmic shopping cart comprises a first historical set of items, the first historical set of items determined by the algorithm, based on a first historical set of inputs comprising information collected from the sensors during a first historical shopping session in the physical store, to have been selected during the first historical shopping session; the first historical virtual shopping cart comprises a second historical set of items associated with the first historical shopping session, wherein video of the first historical shopping session was captured by the set of cameras located in the physical store, the video of the first historical shopping session depicting the second historical set of items being selected; and the first historical set of items matches the second historical set of items; and a second label, labelling a second historical algorithmic shopping cart as inaccurate, indicates that the second historical algorithmic shopping cart does not match a second historical virtual shopping cart, wherein: the second historical algorithmic shopping cart comprises a third historical set of items, the third historical set of items determined by the algorithm, based on a second historical set of inputs comprising information collected from the sensors during a second historical shopping session in the physical store, to have been selected during the second historical shopping session; and the second historical virtual shopping cart comprises a fourth historical set of items associated with the second historical shopping session, wherein video of the second historical shopping session was captured by the set of cameras located in the physical store, the video of the second historical shopping session depicting the fourth historical set of items being selected; and the third historical set of items does not match the fourth historical set of items.
 18. The apparatus of claim 16, wherein the supervised learning algorithm is a neural network.
 19. The apparatus of claim 15, wherein the machine learning algorithm is a reinforcement learning algorithm.
 20. The apparatus of claim 19, wherein the reinforcement learning algorithm is a double deep-Q network. 